The 9 Parts of Speech: Definitions and Examples

  • Ph.D., Rhetoric and English, University of Georgia
  • M.A., Modern English and American Literature, University of Leicester
  • B.A., English, State University of New York

A part of speech is a term used in traditional grammar for one of the nine main categories into which words are classified according to their functions in sentences, such as nouns or verbs. Also known as word classes, these are the building blocks of grammar.

Every sentence you write or speak in English includes words that fall into some of the nine parts of speech. These include nouns, pronouns, verbs, adjectives, adverbs, prepositions, conjunctions, articles/determiners, and interjections. (Some sources include only eight parts of speech and leave interjections in their own category.)

Parts of Speech

  • Word types can be divided into nine parts of speech:
  • prepositions
  • conjunctions
  • articles/determiners
  • interjections
  • Some words can be considered more than one part of speech, depending on context and usage.
  • Interjections can form complete sentences on their own.

Learning the names of the parts of speech probably won't make you witty, healthy, wealthy, or wise. In fact, learning just the names of the parts of speech won't even make you a better writer. However, you will gain a basic understanding of sentence structure  and the  English language by familiarizing yourself with these labels.

Open and Closed Word Classes

The parts of speech are commonly divided into  open classes  (nouns, verbs, adjectives, and adverbs) and  closed classes  (pronouns, prepositions, conjunctions, articles/determiners, and interjections). Open classes can be altered and added to as language develops, and closed classes are pretty much set in stone. For example, new nouns are created every day, but conjunctions never change.

In contemporary linguistics , parts of speech are generally referred to as word classes or syntactic categories. The main difference is that word classes are classified according to more strict linguistic criteria. Within word classes, there is the lexical, or open class, and the function, or closed class.

The 9 Parts of Speech

Read about each part of speech below, and practice identifying each.

Nouns are a person, place, thing, or idea. They can take on a myriad of roles in a sentence, from the subject of it all to the object of an action. They are capitalized when they're the official name of something or someone, and they're called proper nouns in these cases. Examples: pirate, Caribbean, ship, freedom, Captain Jack Sparrow.

Pronouns stand in for nouns in a sentence . They are more generic versions of nouns that refer only to people. Examples:​  I, you, he, she, it, ours, them, who, which, anybody, ourselves.

Verbs are action words that tell what happens in a sentence. They can also show a sentence subject's state of being ( is , was ). Verbs change form based on tense (present, past) and count distinction (singular or plural). Examples:  sing, dance, believes, seemed, finish, eat, drink, be, became.

Adjectives describe nouns and pronouns. They specify which one, how much, what kind, and more. Adjectives allow readers and listeners to use their senses to imagine something more clearly. Examples:  hot, lazy, funny, unique, bright, beautiful, poor, smooth.

Adverbs describe verbs, adjectives, and even other adverbs. They specify when, where, how, and why something happened and to what extent or how often. Many adjectives can be turned into adjectives by adding the suffix - ly . Examples:  softly, quickly, lazily, often, only, hopefully, sometimes.


Prepositions  show spatial, temporal, and role relations between a noun or pronoun and the other words in a sentence. They come at the start of a prepositional phrase , which contains a preposition and its object. Examples:  up, over, against, by, for, into, close to, out of, apart from.


Conjunctions join words, phrases, and clauses in a sentence. There are coordinating, subordinating, and correlative conjunctions. Examples:  and, but, or, so, yet.

Articles and Determiners

Articles and determiners function like adjectives by modifying nouns, but they are different than adjectives in that they are necessary for a sentence to have proper syntax. Articles and determiners specify and identify nouns, and there are indefinite and definite articles. Examples of articles:  a, an, the ; examples of determiners:  these, that, those, enough, much, few, which, what.

Some traditional grammars have treated articles  as a distinct part of speech. Modern grammars, however, more often include articles in the category of determiners , which identify or quantify a noun. Even though they modify nouns like adjectives, articles are different in that they are essential to the proper syntax of a sentence, just as determiners are necessary to convey the meaning of a sentence, while adjectives are optional.


Interjections are expressions that can stand on their own or be contained within sentences. These words and phrases often carry strong emotions and convey reactions. Examples:  ah, whoops, ouch, yabba dabba do!

How to Determine the Part of Speech

Only interjections ( Hooray! ) have a habit of standing alone; every other part of speech must be contained within a sentence and some are even required in sentences (nouns and verbs). Other parts of speech come in many varieties and may appear just about anywhere in a sentence.

To know for sure what part of speech a word falls into, look not only at the word itself but also at its meaning, position, and use in a sentence.

For example, in the first sentence below,  work  functions as a noun; in the second sentence, a verb; and in the third sentence, an adjective:

  • Bosco showed up for  work  two hours late.
  • The noun  work  is the thing Bosco shows up for.
  • He will have to  work  until midnight.
  • The verb  work  is the action he must perform.
  • His  work  permit expires next month.
  • The  attributive noun  (or converted adjective) work  modifies the noun  permit .

Learning the names and uses of the basic parts of speech is just one way to understand how sentences are constructed.

Dissecting Basic Sentences

To form a basic complete sentence, you only need two elements: a noun (or pronoun standing in for a noun) and a verb. The noun acts as a subject, and the verb, by telling what action the subject is taking, acts as the predicate. 

In the short sentence above,  birds  is the noun and  fly  is the verb. The sentence makes sense and gets the point across.

You can have a sentence with just one word without breaking any sentence formation rules. The short sentence below is complete because it's a verb command with an understood "you" noun.

Here, the pronoun, standing in for a noun, is implied and acts as the subject. The sentence is really saying, "(You) go!"

Constructing More Complex Sentences

Use more parts of speech to add additional information about what's happening in a sentence to make it more complex. Take the first sentence from above, for example, and incorporate more information about how and why birds fly.

  • Birds fly when migrating before winter.

Birds and fly remain the noun and the verb, but now there is more description. 

When  is an adverb that modifies the verb fly.  The word before  is a little tricky because it can be either a conjunction, preposition, or adverb depending on the context. In this case, it's a preposition because it's followed by a noun. This preposition begins an adverbial phrase of time ( before winter ) that answers the question of when the birds migrate . Before is not a conjunction because it does not connect two clauses.

  • A List of Exclamations and Interjections in English
  • Sentence Parts and Sentence Structures
  • 100 Key Terms Used in the Study of Grammar
  • Closed Class Words
  • Word Class in English Grammar
  • Prepositional Phrases in English Grammar
  • Foundations of Grammar in Italian
  • The Top 25 Grammatical Terms
  • Open Class Words in English Grammar
  • Telegraphic Speech
  • What Is an Adverb in English Grammar?
  • Pronoun Definition and Examples
  • What Are the Parts of a Prepositional Phrase?
  • Parts of Speech Printable Worksheets
  • Definition and Examples of Function Words in English
  • Lesson Plan: Label Sentences with Parts of Speech
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How to Write a Feature Article

Last Updated: March 11, 2024 Approved

This article was co-authored by Mary Erickson, PhD . Mary Erickson is a Visiting Assistant Professor at Western Washington University. Mary received her PhD in Communication and Society from the University of Oregon in 2011. She is a member of the Modern Language Association, the National Communication Association, and the Society for Cinema and Media Studies. There are 7 references cited in this article, which can be found at the bottom of the page. wikiHow marks an article as reader-approved once it receives enough positive feedback. This article has 41 testimonials from our readers, earning it our reader-approved status. This article has been viewed 1,463,390 times.

Writing a feature article involves using creativity and research to give a detailed and interesting take on a subject. These types of articles are different from typical news stories in that they often are written in a different style and give much more details and description rather than only stating objective facts. This gives the reader a chance to more fully understand some interesting part of the article's subject. While writing a feature article takes lots of planning, research, and work, doing it well is a great way to creatively write about a topic you are passionate about and is a perfect chance to explore different ways to write.

Choosing a Topic

Step 1 Find a compelling story.

  • Human Interest : Many feature stories focus on an issue as it impacts people. They often focus on one person or a group of people.
  • Profile : This feature type focuses on a specific individual’s character or lifestyle. This type is intended to help the reader feel like they’ve gotten a window into someone’s life. Often, these features are written about celebrities or other public figures.
  • Instructional : How-to feature articles teach readers how to do something. Oftentimes, the writer will write about their own journey to learn a task, such as how to make a wedding cake.
  • Historical : Features that honor historical events or developments are quite common. They are also useful in juxtaposing the past and the present, helping to root the reader in a shared history.
  • Seasonal : Some features are perfect for writing about in certain times of year, such as the beginning of summer vacation or at the winter holidays.
  • Behind the Scenes : These features give readers insight into an unusual process, issue or event. It can introduce them to something that is typically not open to the public or publicized.

Step 4 Consider the audience you’d like to talk to.

Interviewing Subjects

Step 1 Schedule an interview at a time and place convenient for the interviewee.

  • Schedule about 30-45 minutes with this person. Be respectful of their time and don’t take up their whole day. Be sure to confirm the date and time a couple of days ahead of the scheduled interview to make sure the time still works for the interviewee.
  • If your interviewee needs to reschedule, be flexible. Remember, they are being generous with their time and allowing you to talk with them, so be generous with your responses as well. Never make an interviewee feel guilty about needing to reschedule.
  • If you want to observe them doing a job, ask if they can bring you to their workplace. Asking if your interviewee will teach you a short lesson about what they do can also be excellent, as it will give you some knowledge of the experience to use when you write.

Step 2 Prepare for your interview.

  • Be sure to ask your interviewee if it’s okay to audio-record the interview. If you plan to use the audio for any purpose other than for your own purposes writing up the article (such as a podcast that might accompany the feature article), you must tell them and get their consent.
  • Don't pressure the interviewee if they decline audio recording.

Step 6 Confirm details about your interviewee.

  • Another good option is a question that begins Tell me about a time when.... This allows the interviewee to tell you the story that's important to them, and can often produce rich information for your article.

Step 8 Actively listen.

Preparing to Write the Article

Step 1 Choose a format for your article.

  • Start by describing a dramatic moment and then uncover the history that led up to that moment.
  • Use a story-within-a-story format, which relies on a narrator to tell the story of someone else.
  • Start the story with an ordinary moment and trace how the story became unusual.

Step 2 Decide on approximate length for the article.

  • Check with your editor to see how long they would like your article to be.

Step 3 Outline your article.

  • Consider what you absolutely must have in the story and what can be cut. If you are writing a 500-word article, for example, you will likely need to be very selective about what you include, whereas you have a lot more space to write in a 2,500 word article.

Writing the Article

Step 1 Write a hook to open your story.

  • Start with an interesting fact, a quote, or an anecdote for a good hook.
  • Your opening paragraph should only be about 2-3 sentences.

Step 2 Expand on your lead in the second paragraph.

  • Be flexible, however. Sometimes when you write, the flow makes sense in a way that is different from your outline. Be ready to change the direction of your piece if it seems to read better that way.

Step 4 Show, don’t tell.

Finalizing the Article

Step 1 Check for accuracy, and check again.

  • You can choose to incorporate or not incorporate their suggestions.

Step 3 Check spelling and grammar.

  • Consult "The Associated Press Stylebook" for style guidelines, such as how to format numbers, dates, street names, and so on. [7] X Research source

Step 4 Get feedback on the article.

  • If you want to convey slightly more information, write a sub-headline, which is a secondary sentence that builds on the headline.

Step 6 Submit your article by the deadline.

How Do You Come Up With an Interesting Angle For an Article?

Sample Feature Article

what part of speech is feature article

Community Q&A

Community Answer

  • Ask to see a proof of your article before it gets published. This is a chance for you to give one final review of the article and double-check details for accuracy. Thanks Helpful 0 Not Helpful 0

what part of speech is feature article

  • Be sure to represent your subjects fairly and accurately. Feature articles can be problematic if they are telling only one side of a story. If your interviewee makes claims against a person or company, make sure you talk with that person or company. If you print claims against someone, even if it’s your interviewee, you might risk being sued for defamation. [9] X Research source Thanks Helpful 0 Not Helpful 0

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About This Article

Mary Erickson, PhD

To write a feature article, start with a 2-3 sentence paragraph that draws your reader into the story. The second paragraph needs to explain why the story is important so the reader keeps reading, and the rest of the piece needs to follow your outline so you can make sure everything flows together how you intended. Try to avoid excessive quotes, complex language, and opinion, and instead focus on appealing to the reader’s senses so they can immerse themselves in the story. Read on for advice from our Communications reviewer on how to conduct an interview! Did this summary help you? Yes No

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Sat / act prep online guides and tips, understanding the 8 parts of speech: definitions and examples.

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If you’re trying to learn the grammatical rules of English, you’ve probably been asked to learn the parts of speech. But what are parts of speech and how many are there? How do you know which words are classified in each part of speech?

The answers to these questions can be a bit complicated—English is a difficult language to learn and understand. Don’t fret, though! We’re going to answer each of these questions for you with a full guide to the parts of speech that explains the following:

  • What the parts of speech are, including a comprehensive parts of speech list
  • Parts of speech definitions for the individual parts of speech. (If you’re looking for information on a specific part of speech, you can search for it by pressing Command + F, then typing in the part of speech you’re interested in.) 
  • Parts of speech examples
  • A ten question quiz covering parts of speech definitions and parts of speech examples

We’ve got a lot to cover, so let’s begin!

Feature Image: (Gavina S / Wikimedia Commons)


What Are Parts of Speech? 

The parts of speech definitions in English can vary, but here’s a widely accepted one: a part of speech is a category of words that serve a similar grammatical purpose in sentences.  

To make that definition even simpler, a part of speech is just a category for similar types of words . All of the types of words included under a single part of speech function in similar ways when they’re used properly in sentences.

In the English language, it’s commonly accepted that there are 8 parts of speech: nouns, verbs, adjectives, adverbs, pronouns, conjunctions, interjections, and prepositions. Each of these categories plays a different role in communicating meaning in the English language. Each of the eight parts of speech—which we might also call the “main classes” of speech—also have subclasses. In other words, we can think of each of the eight parts of speech as being general categories for different types within their part of speech . There are different types of nouns, different types of verbs, different types of adjectives, adverbs, get the idea. 

And that’s an overview of what a part of speech is! Next, we’ll explain each of the 8 parts of speech—definitions and examples included for each category. 


There are tons of nouns in this picture. Can you find them all? 

Nouns are a class of words that refer, generally, to people and living creatures, objects, events, ideas, states of being, places, and actions. You’ve probably heard English nouns referred to as “persons, places, or things.” That definition is a little simplistic, though—while nouns do include people, places, and things, “things” is kind of a vague term. I t’s important to recognize that “things” can include physical things—like objects or belongings—and nonphysical, abstract things—like ideas, states of existence, and actions. 

Since there are many different types of nouns, we’ll include several examples of nouns used in a sentence while we break down the subclasses of nouns next!

Subclasses of Nouns, Including Examples

As an open class of words, the category of “nouns” has a lot of subclasses. The most common and important subclasses of nouns are common nouns, proper nouns, concrete nouns, abstract nouns, collective nouns, and count and mass nouns. Let’s break down each of these subclasses!

Common Nouns and Proper Nouns

Common nouns are generic nouns—they don’t name specific items. They refer to people (the man, the woman), living creatures (cat, bird), objects (pen, computer, car), events (party, work), ideas (culture, freedom), states of being (beauty, integrity), and places (home, neighborhood, country) in a general way. 

Proper nouns are sort of the counterpart to common nouns. Proper nouns refer to specific people, places, events, or ideas. Names are the most obvious example of proper nouns, like in these two examples: 

Common noun: What state are you from?

Proper noun: I’m from Arizona .

Whereas “state” is a common noun, Arizona is a proper noun since it refers to a specific state. Whereas “the election” is a common noun, “Election Day” is a proper noun. Another way to pick out proper nouns: the first letter is often capitalized. If you’d capitalize the word in a sentence, it’s almost always a proper noun. 

Concrete Nouns and Abstract Nouns

Concrete nouns are nouns that can be identified through the five senses. Concrete nouns include people, living creatures, objects, and places, since these things can be sensed in the physical world. In contrast to concrete nouns, abstract nouns are nouns that identify ideas, qualities, concepts, experiences, or states of being. Abstract nouns cannot be detected by the five senses. Here’s an example of concrete and abstract nouns used in a sentence: 

Concrete noun: Could you please fix the weedeater and mow the lawn ?

Abstract noun: Aliyah was delighted to have the freedom to enjoy the art show in peace .

See the difference? A weedeater and the lawn are physical objects or things, and freedom and peace are not physical objects, though they’re “things” people experience! Despite those differences, they all count as nouns. 

Collective Nouns, Count Nouns, and Mass Nouns

Nouns are often categorized based on number and amount. Collective nouns are nouns that refer to a group of something—often groups of people or a type of animal. Team , crowd , and herd are all examples of collective nouns. 

Count nouns are nouns that can appear in the singular or plural form, can be modified by numbers, and can be described by quantifying determiners (e.g. many, most, more, several). For example, “bug” is a count noun. It can occur in singular form if you say, “There is a bug in the kitchen,” but it can also occur in the plural form if you say, “There are many bugs in the kitchen.” (In the case of the latter, you’d call an exterminator...which is an example of a common noun!) Any noun that can accurately occur in one of these singular or plural forms is a count noun. 

Mass nouns are another type of noun that involve numbers and amount. Mass nouns are nouns that usually can’t be pluralized, counted, or quantified and still make sense grammatically. “Charisma” is an example of a mass noun (and an abstract noun!). For example, you could say, “They’ve got charisma, ” which doesn’t imply a specific amount. You couldn’t say, “They’ve got six charismas, ” or, “They’ve got several charismas .” It just doesn’t make sense! 


Verbs are all about action...just like these runners. 

A verb is a part of speech that, when used in a sentence, communicates an action, an occurrence, or a state of being . In sentences, verbs are the most important part of the predicate, which explains or describes what the subject of the sentence is doing or how they are being. And, guess what? All sentences contain verbs!

There are many words in the English language that are classified as verbs. A few common verbs include the words run, sing, cook, talk, and clean. These words are all verbs because they communicate an action performed by a living being. We’ll look at more specific examples of verbs as we discuss the subclasses of verbs next!

Subclasses of Verbs, Including Examples

Like nouns, verbs have several subclasses. The subclasses of verbs include copular or linking verbs, intransitive verbs, transitive verbs, and ditransitive or double transitive verbs. Let’s dive into these subclasses of verbs!

Copular or Linking Verbs

Copular verbs, or linking verbs, are verbs that link a subject with its complement in a sentence. The most familiar linking verb is probably be. Here’s a list of other common copular verbs in English: act, be, become, feel, grow, seem, smell, and taste. 

So how do copular verbs work? Well, in a sentence, if we said, “Michi is ,” and left it at that, it wouldn’t make any sense. “Michi,” the subject, needs to be connected to a complement by the copular verb “is.” Instead, we could say, “Michi is leaving.” In that instance, is links the subject of the sentence to its complement. 

Transitive Verbs, Intransitive Verbs, and Ditransitive Verbs

Transitive verbs are verbs that affect or act upon an object. When unattached to an object in a sentence, a transitive verb does not make sense. Here’s an example of a transitive verb attached to (and appearing before) an object in a sentence: 

Please take the clothes to the dry cleaners.

In this example, “take” is a transitive verb because it requires an object—”the clothes”—to make sense. “The clothes” are the objects being taken. “Please take” wouldn’t make sense by itself, would it? That’s because the transitive verb “take,” like all transitive verbs, transfers its action onto another being or object. 

Conversely, intransitive verbs don’t require an object to act upon in order to make sense in a sentence. These verbs make sense all on their own! For instance, “They ran ,” “We arrived ,” and, “The car stopped ” are all examples of sentences that contain intransitive verbs. 

Finally, ditransitive verbs, or double transitive verbs, are a bit more complicated. Ditransitive verbs are verbs that are followed by two objects in a sentence . One of the objects has the action of the ditransitive verb done to it, and the other object has the action of the ditransitive verb directed towards it. Here’s an example of what that means in a sentence: 

I cooked Nathan a meal.

In this example, “cooked” is a ditransitive verb because it modifies two objects: Nathan and meal . The meal has the action of “cooked” done to it, and “Nathan” has the action of the verb directed towards him. 


Adjectives are descriptors that help us better understand a sentence. A common adjective type is color.

#3: Adjectives

Here’s the simplest definition of adjectives: adjectives are words that describe other words . Specifically, adjectives modify nouns and noun phrases. In sentences, adjectives appear before nouns and pronouns (they have to appear before the words they describe!). 

Adjectives give more detail to nouns and pronouns by describing how a noun looks, smells, tastes, sounds, or feels, or its state of being or existence. . For example, you could say, “The girl rode her bike.” That sentence doesn’t have any adjectives in it, but you could add an adjective before both of the nouns in the sentence—”girl” and “bike”—to give more detail to the sentence. It might read like this: “The young girl rode her red bike.”   You can pick out adjectives in a sentence by asking the following questions: 

  • Which one? 
  • What kind? 
  • How many? 
  • Whose’s? 

We’ll look at more examples of adjectives as we explore the subclasses of adjectives next!

Subclasses of Adjectives, Including Examples

Subclasses of adjectives include adjective phrases, comparative adjectives, superlative adjectives, and determiners (which include articles, possessive adjectives, and demonstratives). 

Adjective Phrases

An adjective phrase is a group of words that describe a noun or noun phrase in a sentence. Adjective phrases can appear before the noun or noun phrase in a sentence, like in this example: 

The extremely fragile vase somehow did not break during the move.

In this case, extremely fragile describes the vase. On the other hand, adjective phrases can appear after the noun or noun phrase in a sentence as well: 

The museum was somewhat boring. 

Again, the phrase somewhat boring describes the museum. The takeaway is this: adjective phrases describe the subject of a sentence with greater detail than an individual adjective. 

Comparative Adjectives and Superlative Adjectives

Comparative adjectives are used in sentences where two nouns are compared. They function to compare the differences between the two nouns that they modify. In sentences, comparative adjectives often appear in this pattern and typically end with -er. If we were to describe how comparative adjectives function as a formula, it might look something like this: 

Noun (subject) + verb + comparative adjective + than + noun (object).

Here’s an example of how a comparative adjective would work in that type of sentence: 

The horse was faster than the dog.

The adjective faster compares the speed of the horse to the speed of the dog. Other common comparative adjectives include words that compare distance ( higher, lower, farther ), age ( younger, older ), size and dimensions ( bigger, smaller, wider, taller, shorter ), and quality or feeling ( better, cleaner, happier, angrier ). 

Superlative adjectives are adjectives that describe the extremes of a quality that applies to a subject being compared to a group of objects . Put more simply, superlative adjectives help show how extreme something is. In sentences, superlative adjectives usually appear in this structure and end in -est : 

Noun (subject) + verb + the + superlative adjective + noun (object).

Here’s an example of a superlative adjective that appears in that type of sentence: 

Their story was the funniest story. 

In this example, the subject— story —is being compared to a group of objects—other stories. The superlative adjective “funniest” implies that this particular story is the funniest out of all the stories ever, period. Other common superlative adjectives are best, worst, craziest, and happiest... though there are many more than that! 

It’s also important to know that you can often omit the object from the end of the sentence when using superlative adjectives, like this: “Their story was the funniest.” We still know that “their story” is being compared to other stories without the object at the end of the sentence.


The last subclass of adjectives we want to look at are determiners. Determiners are words that determine what kind of reference a noun or noun phrase makes. These words are placed in front of nouns to make it clear what the noun is referring to. Determiners are an example of a part of speech subclass that contains a lot of subclasses of its own. Here is a list of the different types of determiners: 

  • Definite article: the
  • Indefinite articles : a, an 
  • Demonstratives: this, that, these, those
  • Pronouns and possessive determiners: my, your, his, her, its, our, their
  • Quantifiers : a little, a few, many, much, most, some, any, enough
  • Numbers: one, twenty, fifty
  • Distributives: all, both, half, either, neither, each, every
  • Difference words : other, another
  • Pre-determiners: such, what, rather, quite

Here are some examples of how determiners can be used in sentences: 

Definite article: Get in the car.  

Demonstrative: Could you hand me that magazine?  

Possessive determiner: Please put away your clothes. 

Distributive: He ate all of the pie. 

Though some of the words above might not seem descriptive, they actually do describe the specificity and definiteness, relationship, and quantity or amount of a noun or noun phrase. For example, the definite article “the” (a type of determiner) indicates that a noun refers to a specific thing or entity. The indefinite article “an,” on the other hand, indicates that a noun refers to a nonspecific entity. 

One quick note, since English is always more complicated than it seems: while articles are most commonly classified as adjectives, they can also function as adverbs in specific situations, too. Not only that, some people are taught that determiners are their own part of speech...which means that some people are taught there are 9 parts of speech instead of 8! 

It can be a little confusing, which is why we have a whole article explaining how articles function as a part of speech to help clear things up . 


Adverbs can be used to answer questions like "when?" and "how long?"

Adverbs are words that modify verbs, adjectives (including determiners), clauses, prepositions, and sentences. Adverbs typically answer the questions how?, in what way?, when?, where?, and to what extent? In answering these questions, adverbs function to express frequency, degree, manner, time, place, and level of certainty . Adverbs can answer these questions in the form of single words, or in the form of adverbial phrases or adverbial clauses. 

Adverbs are commonly known for being words that end in -ly, but there’s actually a bit more to adverbs than that, which we’ll dive into while we look at the subclasses of adverbs!

Subclasses Of Adverbs, Including Examples

There are many types of adverbs, but the main subclasses we’ll look at are conjunctive adverbs, and adverbs of place, time, manner, degree, and frequency. 

Conjunctive Adverbs

Conjunctive adverbs look like coordinating conjunctions (which we’ll talk about later!), but they are actually their own category: conjunctive adverbs are words that connect independent clauses into a single sentence . These adverbs appear after a semicolon and before a comma in sentences, like in these two examples: 

She was exhausted; nevertheless , she went for a five mile run. 

They didn’t call; instead , they texted.  

Though conjunctive adverbs are frequently used to create shorter sentences using a semicolon and comma, they can also appear at the beginning of sentences, like this: 

He chopped the vegetables. Meanwhile, I boiled the pasta.  

One thing to keep in mind is that conjunctive adverbs come with a comma. When you use them, be sure to include a comma afterward! 

There are a lot of conjunctive adverbs, but some common ones include also, anyway, besides, finally, further, however, indeed, instead, meanwhile, nevertheless, next, nonetheless, now, otherwise, similarly, then, therefore, and thus.  

Adverbs of Place, Time, Manner, Degree, and Frequency

There are also adverbs of place, time, manner, degree, and frequency. Each of these types of adverbs express a different kind of meaning. 

Adverbs of place express where an action is done or where an event occurs. These are used after the verb, direct object, or at the end of a sentence. A sentence like “She walked outside to watch the sunset” uses outside as an adverb of place. 

Adverbs of time explain when something happens. These adverbs are used at the beginning or at the end of sentences. In a sentence like “The game should be over soon,” soon functions as an adverb of time. 

Adverbs of manner describe the way in which something is done or how something happens. These are the adverbs that usually end in the familiar -ly.  If we were to write “She quickly finished her homework,” quickly is an adverb of manner. 

Adverbs of degree tell us the extent to which something happens or occurs. If we were to say “The play was quite interesting,” quite tells us the extent of how interesting the play was. Thus, quite is an adverb of degree.  

Finally, adverbs of frequency express how often something happens . In a sentence like “They never know what to do with themselves,” never is an adverb of frequency. 

Five subclasses of adverbs is a lot, so we’ve organized the words that fall under each category in a nifty table for you here: 

It’s important to know about these subclasses of adverbs because many of them don’t follow the old adage that adverbs end in -ly. 


Here's a helpful list of pronouns. (Attanata / Flickr )

#5: Pronouns

Pronouns are words that can be substituted for a noun or noun phrase in a sentence . Pronouns function to make sentences less clunky by allowing people to avoid repeating nouns over and over. For example, if you were telling someone a story about your friend Destiny, you wouldn’t keep repeating their name over and over again every time you referred to them. Instead, you’d use a pronoun—like they or them—to refer to Destiny throughout the story. 

Pronouns are typically short words, often only two or three letters long. The most familiar pronouns in the English language are they, she, and he. But these aren’t the only pronouns. There are many more pronouns in English that fall under different subclasses!

Subclasses of Pronouns, Including Examples

There are many subclasses of pronouns, but the most commonly used subclasses are personal pronouns, possessive pronouns, demonstrative pronouns, indefinite pronouns, and interrogative pronouns. 

Personal Pronouns

Personal pronouns are probably the most familiar type of pronoun. Personal pronouns include I, me, you, she, her, him, he, we, us, they, and them. These are called personal pronouns because they refer to a person! Personal pronouns can replace specific nouns in sentences, like a person’s name, or refer to specific groups of people, like in these examples: 

Did you see Gia pole vault at the track meet? Her form was incredible!

The Cycling Club is meeting up at six. They said they would be at the park. 

In both of the examples above, a pronoun stands in for a proper noun to avoid repetitiveness. Her replaces Gia in the first example, and they replaces the Cycling Club in the second example. 

(It’s also worth noting that personal pronouns are one of the easiest ways to determine what point of view a writer is using.) 

Possessive Pronouns

Possessive pronouns are used to indicate that something belongs to or is the possession of someone. The possessive pronouns fall into two categories: limiting and absolute. In a sentence, absolute possessive pronouns can be substituted for the thing that belongs to a person, and limiting pronouns cannot. 

The limiting pronouns are my, your, its, his, her, our, their, and whose, and the absolute pronouns are mine, yours, his, hers, ours, and theirs . Here are examples of a limiting possessive pronoun and absolute possessive pronoun used in a sentence: 

Limiting possessive pronoun: Juan is fixing his car. 

In the example above, the car belongs to Juan, and his is the limiting possessive pronoun that shows the car belongs to Juan. Now, here’s an example of an absolute pronoun in a sentence: 

Absolute possessive pronoun: Did you buy your tickets ? We already bought ours . 

In this example, the tickets belong to whoever we is, and in the second sentence, ours is the absolute possessive pronoun standing in for the thing that “we” possess—the tickets. 

Demonstrative Pronouns, Interrogative Pronouns, and Indefinite Pronouns

Demonstrative pronouns include the words that, this, these, and those. These pronouns stand in for a noun or noun phrase that has already been mentioned in a sentence or conversation. This and these are typically used to refer to objects or entities that are nearby distance-wise, and that and those usually refer to objects or entities that are farther away. Here’s an example of a demonstrative pronoun used in a sentence: 

The books are stacked up in the garage. Can you put those away? 

The books have already been mentioned, and those is the demonstrative pronoun that stands in to refer to them in the second sentence above. The use of those indicates that the books aren’t nearby—they’re out in the garage. Here’s another example: 

Do you need shoes? can borrow these. 

In this sentence, these refers to the noun shoes. Using the word these tells readers that the shoes are nearby...maybe even on the speaker’s feet! 

Indefinite pronouns are used when it isn’t necessary to identify a specific person or thing . The indefinite pronouns are one, other, none, some, anybody, everybody, and no one. Here’s one example of an indefinite pronoun used in a sentence: 

Promise you can keep a secret? 

Of course. I won’t tell anyone. 

In this example, the person speaking in the second two sentences isn’t referring to any particular people who they won’t tell the secret to. They’re saying that, in general, they won’t tell anyone . That doesn’t specify a specific number, type, or category of people who they won’t tell the secret to, which is what makes the pronoun indefinite. 

Finally, interrogative pronouns are used in questions, and these pronouns include who, what, which, and whose. These pronouns are simply used to gather information about specific nouns—persons, places, and ideas. Let’s look at two examples of interrogative pronouns used in sentences: 

Do you remember which glass was mine? 

What time are they arriving? 

In the first glass, the speaker wants to know more about which glass belongs to whom. In the second sentence, the speaker is asking for more clarity about a specific time. 


Conjunctions hook phrases and clauses together so they fit like pieces of a puzzle.

#6: Conjunctions

Conjunctions are words that are used to connect words, phrases, clauses, and sentences in the English language. This function allows conjunctions to connect actions, ideas, and thoughts as well. Conjunctions are also used to make lists within sentences. (Conjunctions are also probably the most famous part of speech, since they were immortalized in the famous “Conjunction Junction” song from Schoolhouse Rock .) 

You’re probably familiar with and, but, and or as conjunctions, but let’s look into some subclasses of conjunctions so you can learn about the array of conjunctions that are out there!

Subclasses of Conjunctions, Including Examples

Coordinating conjunctions, subordinating conjunctions, and correlative conjunctions are three subclasses of conjunctions. Each of these types of conjunctions functions in a different way in sentences!

Coordinating Conjunctions

Coordinating conjunctions are probably the most familiar type of conjunction. These conjunctions include the words for, and, nor, but, or, yet, so (people often recommend using the acronym FANBOYS to remember the seven coordinating conjunctions!). 

Coordinating conjunctions are responsible for connecting two independent clauses in sentences, but can also be used to connect two words in a sentence. Here are two examples of coordinating conjunctions that connect two independent clauses in a sentence: 

He wanted to go to the movies, but he couldn’t find his car keys. 

They put on sunscreen, and they went to the beach. 

Next, here are two examples of coordinating conjunctions that connect two words: 

Would you like to cook or order in for dinner? 

The storm was loud yet refreshing. 

The two examples above show that coordinating conjunctions can connect different types of words as well. In the first example, the coordinating conjunction “or” connects two verbs; in the second example, the coordinating conjunction “yet” connects two adjectives. 

But wait! Why does the first set of sentences have commas while the second set of sentences doesn’t? When using a coordinating conjunction, put a comma before the conjunction when it’s connecting two complete sentences . Otherwise, there’s no comma necessary. 

Subordinating Conjunctions

Subordinating conjunctions are used to link an independent clause to a dependent clause in a sentence. This type of conjunction always appears at the beginning of a dependent clause, which means that subordinating conjunctions can appear at the beginning of a sentence or in the middle of a sentence following an independent clause. (If you’re unsure about what independent and dependent clauses are, be sure to check out our guide to compound sentences.) 

Here is an example of a subordinating conjunction that appears at the beginning of a sentence: 

Because we were hungry, we ordered way too much food. 

Now, here’s an example of a subordinating conjunction that appears in the middle of a sentence, following an independent clause and a comma: 

Rakim was scared after the power went out. 

See? In the example above, the subordinating conjunction after connects the independent clause Rakim was scared to the dependent clause after the power went out. Subordinating conjunctions include (but are not limited to!) the following words: after, as, because, before, even though, one, since, unless, until, whenever, and while. 

Correlative Conjunctions

Finally, correlative conjunctions are conjunctions that come in pairs, like both/and, either/or, and neither/nor. The two correlative conjunctions that come in a pair must appear in different parts of a sentence to make sense— they correlate the meaning in one part of the sentence with the meaning in another part of the sentence . Makes sense, right? 

Here are two examples of correlative conjunctions used in a sentence: 

We’re either going to the Farmer’s Market or the Natural Grocer’s for our shopping today. 

They’re going to have to get dog treats for both Piper and Fudge. 

Other pairs of correlative conjunctions include as many/as, not/but, not only/but also, rather/than, such/that, and whether/or. 


Interjections are single words that express emotions that end in an exclamation point. Cool!

#7: Interjections 

Interjections are words that often appear at the beginning of sentences or between sentences to express emotions or sentiments such as excitement, surprise, joy, disgust, anger, or even pain. Commonly used interjections include wow!, yikes!, ouch!, or ugh! One clue that an interjection is being used is when an exclamation point appears after a single word (but interjections don’t have to be followed by an exclamation point). And, since interjections usually express emotion or feeling, they’re often referred to as being exclamatory. Wow! 

Interjections don’t come together with other parts of speech to form bigger grammatical units, like phrases or clauses. There also aren’t strict rules about where interjections should appear in relation to other sentences . While it’s common for interjections to appear before sentences that describe an action or event that the interjection helps explain, interjections can appear after sentences that contain the action they’re describing as well. 

Subclasses of Interjections, Including Examples

There are two main subclasses of interjections: primary interjections and secondary interjections. Let’s take a look at these two types of interjections!

Primary Interjections  

Primary interjections are single words, like oh!, wow!, or ouch! that don’t enter into the actual structure of a sentence but add to the meaning of a sentence. Here’s an example of how a primary interjection can be used before a sentence to add to the meaning of the sentence that follows it: 

Ouch ! I just burned myself on that pan!

While someone who hears, I just burned myself on that pan might assume that the person who said that is now in pain, the interjection Ouch! makes it clear that burning oneself on the pan definitely was painful. 

Secondary Interjections

Secondary interjections are words that have other meanings but have evolved to be used like interjections in the English language and are often exclamatory. Secondary interjections can be mixed with greetings, oaths, or swear words. In many cases, the use of secondary interjections negates the original meaning of the word that is being used as an interjection. Let’s look at a couple of examples of secondary interjections here: 

Well , look what the cat dragged in!

Heck, I’d help if I could, but I’ve got to get to work. 

You probably know that the words well and heck weren’t originally used as interjections in the English language. Well originally meant that something was done in a good or satisfactory way, or that a person was in good health. Over time and through repeated usage, it’s come to be used as a way to express emotion, such as surprise, anger, relief, or resignation, like in the example above. 


This is a handy list of common prepositional phrases. (attanatta / Flickr) 

#8: Prepositions

The last part of speech we’re going to define is the preposition. Prepositions are words that are used to connect other words in a sentence—typically nouns and verbs—and show the relationship between those words. Prepositions convey concepts such as comparison, position, place, direction, movement, time, possession, and how an action is completed. 

Subclasses of Prepositions, Including Examples

The subclasses of prepositions are simple prepositions, double prepositions, participle prepositions, and prepositional phrases. 

Simple Prepositions

Simple prepositions appear before and between nouns, adjectives, or adverbs in sentences to convey relationships between people, living creatures, things, or places . Here are a couple of examples of simple prepositions used in sentences: 

I’ll order more ink before we run out. 

Your phone was beside your wallet. 

In the first example, the preposition before appears between the noun ink and the personal pronoun we to convey a relationship. In the second example, the preposition beside appears between the verb was and the possessive pronoun your.

In both examples, though, the prepositions help us understand how elements in the sentence are related to one another. In the first sentence, we know that the speaker currently has ink but needs more before it’s gone. In the second sentence, the preposition beside helps us understand how the wallet and the phone are positioned relative to one another! 

Double Prepositions

Double prepositions are exactly what they sound like: two prepositions joined together into one unit to connect phrases, nouns, and pronouns with other words in a sentence. Common examples of double prepositions include outside of, because of, according to, next to, across from, and on top of. Here is an example of a double preposition in a sentence: 

I thought you were sitting across from me. 

You see? Across and from both function as prepositions individually. When combined together in a sentence, they create a double preposition. (Also note that the prepositions help us understand how two people— you and I— are positioned with one another through spacial relationship.)  

Prepositional Phrases

Finally, prepositional phrases are groups of words that include a preposition and a noun or pronoun. Typically, the noun or pronoun that appears after the preposition in a prepositional phrase is called the object of the preposition. The object always appears at the end of the prepositional phrase. Additionally, prepositional phrases never include a verb or a subject. Here are two examples of prepositional phrases: 

The cat sat under the chair . 

In the example above, “under” is the preposition, and “the chair” is the noun, which functions as the object of the preposition. Here’s one more example: 

We walked through the overgrown field . 

Now, this example demonstrates one more thing you need to know about prepositional phrases: they can include an adjective before the object. In this example, “through” is the preposition, and “field” is the object. “Overgrown” is an adjective that modifies “the field,” and it’s quite common for adjectives to appear in prepositional phrases like the one above. 

While that might sound confusing, don’t worry: the key is identifying the preposition in the first place! Once you can find the preposition, you can start looking at the words around it to see if it forms a compound preposition, a double preposition of a prepositional phrase. 


10 Question Quiz: Test Your Knowledge of Parts of Speech Definitions and Examples

Since we’ve covered a lot of material about the 8 parts of speech with examples ( a lot of them!), we want to give you an opportunity to review and see what you’ve learned! While it might seem easier to just use a parts of speech finder instead of learning all this stuff, our parts of speech quiz can help you continue building your knowledge of the 8 parts of speech and master each one. 

Are you ready? Here we go:  

1) What are the 8 parts of speech? 

a) Noun, article, adverb, antecedent, verb, adjective, conjunction, interjection b) Noun, pronoun, verb, adverb, determiner, clause, adjective, preposition c) Noun, verb, adjective, adverb, pronoun, conjunction, interjection, preposition

2) Which parts of speech have subclasses?

a) Nouns, verbs, adjectives, and adverbs b) Nouns, verbs, adjectives, adverbs, conjunctions, and prepositions c) All of them! There are many types of words within each part of speech.

3) What is the difference between common nouns and proper nouns?

a) Common nouns don’t refer to specific people, places, or entities, but proper nouns do refer to specific people, places, or entities.  b) Common nouns refer to regular, everyday people, places, or entities, but proper nouns refer to famous people, places, or entities.  c) Common nouns refer to physical entities, like people, places, and objects, but proper nouns refer to nonphysical entities, like feelings, ideas, and experiences.

4) In which of the following sentences is the emboldened word a verb?

a) He was frightened by the horror film .   b) He adjusted his expectations after the first plan fell through.  c) She walked briskly to get there on time.

5) Which of the following is a correct definition of adjectives, and what other part of speech do adjectives modify?

a) Adjectives are describing words, and they modify nouns and noun phrases.  b) Adjectives are describing words, and they modify verbs and adverbs.  c) Adjectives are describing words, and they modify nouns, verbs, and adverbs.

6) Which of the following describes the function of adverbs in sentences?

a) Adverbs express frequency, degree, manner, time, place, and level of certainty. b) Adverbs express an action performed by a subject.  c) Adverbs describe nouns and noun phrases.

7) Which of the following answers contains a list of personal pronouns?

a) This, that, these, those b) I, you, me, we, he, she, him, her, they, them c) Who, what, which, whose

8) Where do interjections typically appear in a sentence?

a) Interjections can appear at the beginning of or in between sentences. b) Interjections appear at the end of sentences.  c) Interjections appear in prepositional phrases.

9) Which of the following sentences contains a prepositional phrase?

a) The dog happily wagged his tail.  b) The cow jumped over the moon.  c) She glared, angry that he forgot the flowers.

10) Which of the following is an accurate definition of a “part of speech”?

a) A category of words that serve a similar grammatical purpose in sentences. b) A category of words that are of similar length and spelling. c) A category of words that mean the same thing.

So, how did you do? If you got 1C, 2C, 3A, 4B, 5A, 6A, 7B, 8A, 9B, and 10A, you came out on top! There’s a lot to remember where the parts of speech are concerned, and if you’re looking for more practice like our quiz, try looking around for parts of speech games or parts of speech worksheets online!


What’s Next?

You might be brushing up on your grammar so you can ace the verbal portions of the SAT or ACT. Be sure you check out our guides to the grammar you need to know before you tackle those tests! Here’s our expert guide to the grammar rules you need to know for the SAT , and this article teaches you the 14 grammar rules you’ll definitely see on the ACT.

When you have a good handle on parts of speech, it can make writing essays tons easier. Learn how knowing parts of speech can help you get a perfect 12 on the ACT Essay (or an 8/8/8 on the SAT Essay ).

While we’re on the topic of grammar: keep in mind that knowing grammar rules is only part of the battle when it comes to the verbal and written portions of the SAT and ACT. Having a good vocabulary is also important to making the perfect score ! Here are 262 vocabulary words you need to know before you tackle your standardized tests.

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Ashley Sufflé Robinson has a Ph.D. in 19th Century English Literature. As a content writer for PrepScholar, Ashley is passionate about giving college-bound students the in-depth information they need to get into the school of their dreams.

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  • January 21, 2024

How to Write a Feature Article: Crafting Captivating Stories

Julia mccoy.

Creator and Co-founder

Ever tried your hand at how to write a feature article ? It’s not just about the facts; it’s an art. You’re crafting a window into another world, painting pictures with words that draw readers in and make them care. If you’ve been spinning your wheels, don’t sweat it.

This piece will guide you on how to write a feature article that weaves human experiences into life stories that resonate. From choosing the right angle to hitting hard with an impactful narrative structure, we’ll show how lifestyle features, travel narratives, or profile pieces can turn into compelling reads.

You’ll learn tips for punchy openings and satisfying endings that leave readers thinking long after they’ve finished reading.

Ready? Let’s dive in!

Table Of Contents:

What are feature articles, 10 different types of feature articles, how to write a feature article: a step-by-step guide, tips on how to write a great feature article, start writing feature articles like a pro.

A feature story is not your run-of-the-mill news piece.

It paints pictures with words, captures emotions, and weaves facts into narratives that hit home.

This genre offers readers an escape from the blunt edges of hard news by infusing human experience into storytelling.

The ever-evolving world of journalism reveals just how potent these stories can be when they bridge connections between the subject and the audience.

In stark contrast to straight news, feature stories give you more than who, what, where, and when; they delve into the why and how.

You get richly textured pieces like lifestyle features or travel adventures rather than bullet-pointed briefs on world headlines. They’re akin to a stroll through intriguing alleys rather than a brisk walk down Main Street.

With each paragraph designed to evoke feelings rather than simply relay events, it’s no surprise that people are drawn to such compelling reads.

And remember: at their core, feature stories aim for emotional impact, connecting on levels beyond mere information exchange. To create this effect, writers often employ descriptive language and narrative techniques that have been proven effective over time.

Your role model might be Pulitzer Prize winners or leather-jacket-clad journalists typing away in coffee shops. But whatever form inspiration takes, keep one thing clear: good writing starts with solid research grounded in real-world perspectives.

The world of content marketing is diverse and dynamic, offering a wide range of possibilities for entrepreneurs looking to grow their businesses. One powerful tool in your arsenal should be the feature article.

Feature articles come in various forms, each with its unique approach and purpose. Here’s a brief overview of 10 different types of feature stories you can write for your audience.

1. Human Interest Stories

A human interest story centers on individuals or groups, focusing on personal achievements, dramatic events, or everyday life struggles. The goal here is to evoke emotion from readers and create an engaging narrative around people’s experiences.

2. News Features

News features, arguably the most common type of feature articles, delve into current events providing detailed explanations behind these happenings while examining potential implications. These stories are not just about reporting facts but also providing context and analysis.

3. Lifestyle Features

Focusing on how life can be improved or enjoyed more fully, lifestyle features offer tips and advice ranging from fitness routines to meditation techniques. They aim to enhance your readers’ lives by offering practical solutions for common problems or introducing them to new ideas that might enrich their day-to-day lives.

4. Seasonal Features

These articles focus on events, activities, or topics that are relevant to a particular season, such as holidays, festivals, or seasonal trends.

Whether you’re a journalist or content creator, you probably have a scheduled calendar that designates deadlines for various types of feature articles. One notable advantage of these features is the ability to plan and structure them, a luxury not often afforded with conventional news stories.

5. Interview Pieces

In this type of feature, the writer conducts interviews with individuals to gather insights, opinions, and personal stories. The article often presents a narrative based on these interviews.

6. Color Stories

Color stories go beyond the facts and atmosphere of hard news, often serving as companions to news articles.

Skillful feature writing in this context enables readers to vividly envision the experience of being at a particular event, fostering a deeper understanding of the issues and implications embedded in a story.

7. Profile Features

Profile features center around a specific person, providing an in-depth look into their life, achievements, challenges, and personality. These articles are like mini-biographies that seek to humanize and bring the subject to life.

8. Behind The Scenes

Behind-the-scenes features take readers into places or processes not typically visible to the public. This type of article provides insights into how something is made, accomplished, or organized.

9. Travel Features

Travel features explore destinations, cultures, and experiences. They often include personal anecdotes, recommendations, and practical information for readers interested in exploring the featured location.

10. Instructional Features

Instructional features provide readers with step-by-step guidance, advice, or information on how to do something. These articles aim to educate and empower the audience with practical knowledge.

‘How-to’ features have gained increased popularity, especially in the era of internet ‘life hacks.’ There is now a subcategory of these features where writers experiment with instructional content and share their insights on its practicality.

You don’t need to look too far to find an instructional feature article – you are currently reading one.

These types of feature articles offer diverse ways to present information, capture readers’ attention, and tell compelling stories. Depending on the subject matter and the target audience, writers can choose the most suitable format to convey their message effectively.

A feature article is an excellent tool to provide in-depth information about a topic, person, or event. Here’s how you can write one effectively:

Step 1: Evaluate Your Story Ideas

The first step in how to write a feature article is to flesh out your ideas. These are the seeds from which your story will grow.

But what if you’re staring at a blank page, bereft of inspiration? This is where renowned publications like The New York Times ‘Trending’ section or The Guardian’s Features can serve as fertile ground for ideas.

However, remember that these sources should be used purely for educational purposes and inspiration – never copy or plagiarize content. The goal here is not to replicate but rather to stimulate your creative juices by reading about diverse topics and unique storytelling methods.

You can also use an AI tool like Content at Scale to generate ideas or topics that are relevant to your niche.

To effectively evaluate potential story ideas:

  • Analyze Trends: What are people talking about? What issues are making headlines? You could use tools like Google Trends or Buzzsumo to identify trending topics relevant to your industry.
  • Understand Your Audience: Know who you’re writing for — their interests, concerns, and questions. Use this understanding as a compass guiding the direction of your stories.
  • Evaluate Relevance and Value: Your story should ideally offer something new — fresh insights, unexplored angles on familiar themes, or practical solutions. Ask yourself how it adds value to the reader’s life before choosing a story.

Step 2: Do Your Research

Feature stories need more than straight facts and sensory details — they need evidence. This can come in the form of quotes, anecdotes, or interviews.

The significance of these elements cannot be overstated as they lend credibility to your narrative while making it more engaging for readers. Hearing viewpoints from various sources helps make your story feel three-dimensional and thus allows you to craft a vivid tale that resonates with your audience.

  • Quotes: These provide direct insights into people’s thoughts and opinions on the subject matter. They give your piece authenticity and add personal touch points which can evoke empathy among readers.
  • Anecdotes: Anecdotal information serves as illustrative examples that breathe life into statistics or hard data points. They help create emotional connections between readers and subjects.
  • Interviews: Conducting interviews gives you access to first-hand accounts, expert perspectives, or unique angles about your topic that could otherwise remain uncovered.

Step 3: Choose a Feature Type

After doing your research, ask yourself what type of feature article you want to write.

Sometimes, this initial decision can shift as you delve deeper into your research. Perhaps you started out intending to write a lifestyle piece about a local sports team’s fitness regimen but ended up focusing on an inspiring interview with an athlete who transformed their health.

This is not uncommon. It’s part and parcel of content writing where story ideas often evolve based on ongoing reporting and discovery. Embrace these changes as they occur – they might lead you down more interesting paths than you initially envisioned.

Step 4: Select an Appropriate Writing Style

Selecting an appropriate writing style is a critical step in crafting your feature article. Your choice of language and tone will significantly impact how your audience perceives the information you present.

To help get you started, here are a few tips:

  • Embrace Your Unique Style: Your unique voice is what sets you apart from other writers. Don’t be afraid to let it shine through in your articles! For example, if humor comes naturally to you, consider incorporating it into your piece — provided it fits with the topic and overall tone of course.
  • Use Emotive Language: The power of emotive language should not be underestimated when engaging readers on a deeper level. By using words that evoke emotions or sensory experiences, we can create stronger connections with our audience.
  • Mind Your Adjectives & Adverbs: While adjectives and adverbs can add color to our writing, overuse may make the text seem overly embellished or insincere. Be selective about their usage for maximum effect.
  • Speak Directly To The Reader: In most cases, referring directly to the reader as ‘you’ makes them feel more involved in what they’re reading – like they’re part of a conversation rather than being lectured at.

Step 5: Craft a Compelling Headline

The power of your feature article lies not only in its content but also in the strength of its headline. A compelling, catchy title can make all the difference between an overlooked piece and one that attracts readership.

In most cases, you won’t have a dedicated subeditor to help craft this crucial element — it falls on you as the writer or marketer to devise an eye-catching headline that summarizes your story while enticing potential readers.

Creating a captivating header requires time and consideration. It isn’t something to be rushed; rather, it should be seen as an integral part of your writing process.

Tips for creating catchy headlines:

  • Create intrigue: Your goal is to pique curiosity without giving away too much about the story’s content. Think mystery novels – they don’t reveal whodunit on their covers!
  • Use powerful words: Words like ‘Secret’, ‘Free’, and ‘Proven’ are known power words, which trigger emotional responses from readers making them more likely to click through.
  • Pose a question: By asking questions related to your topic, you encourage readers to seek answers within your feature article.

Beyond these tips, another effective strategy involves using intriguing quotes from within the story itself as headers. This technique provides context while generating interest in what else might lie within the body text.

Step 6: Open With Interest

The opening paragraph of your feature article is crucial to drawing in your readers and piquing their interest. It’s the hook that can either reel them in or let them off the line, so it needs to be compelling enough to make them want more.

One method you could use is building tension right from the start. This could involve setting up a conflict or problem that will be resolved later on in the article. The anticipation created by this technique can keep readers engaged as they’re eager to find out what happens next.

You might also consider posing a rhetorical question at the outset — something thought-provoking that encourages readers to think about an issue before diving into your story.

Another way to hook your audience is to make an outlandish statement -– one that may seem absurd initially but gets substantiated as you progress through your content. Outrageous claims are one way to grab attention instantly. Just ensure there’s substance behind such statements, or else your credibility will take a hit!

Lastly, try opening with a significant event familiar to most people and then work backward from there. Explain its relevance and context to your overall theme or argument.

No matter which strategy you employ for crafting compelling introductions, remember: Your primary goal should always be capturing reader interest and making them curious enough to continue reading further into your feature article.

Step 7: Be Creative with Storytelling

Creativity can be a game-changer when it comes to writing feature articles. Unlike traditional news stories that stick to a rigid structure and tone, feature articles offer you ample room for innovation and creativity.

A story should have a beginning, a middle, and an end, but not necessarily in that order. This is particularly applicable to feature articles where there’s flexibility in terms of narrative flow.

In crafting your article, consider playing around with the sequence of information or incorporating elements such as anecdotes or personal experiences that may resonate with your readers on an emotional level.

You could also experiment with different styles — perhaps injecting humor into serious topics or adopting an unconventional perspective on common issues.

While you’re free to explore creative avenues, remember not to lose sight of the core purpose of your feature story: to share valuable information with your audience. The secret is finding the right balance between engaging storytelling and delivering insightful content.

Content Hacker provides more insights into this aspect.

  • Risk-taking: Push boundaries by experimenting with unique ideas or formats that deviate from conventional norms.
  • Audience-centricity: Tailor your creative approach based on what resonates best with your audience – their preferences matter!
  • Balanced approach: Creativity shouldn’t compromise clarity; ensure all key points are effectively communicated within the creative framework.

Step 8: End With A Bang

The best feature writers always leave a little something for the reader at the end of their article. This could be a powerful conclusion or an element that ties everything together, but it’s crucial to provide some sort of closure.

This gives your audience a sense of satisfaction upon finishing your piece and makes them anticipate future articles from you.

The order in which you follow these steps isn’t set in stone, especially if you’re new to this type of writing. However, they should serve as a solid starting point when creating feature articles.

In time, you’ll develop your own style and voice that suits both you and your content perfectly.

Finding success with long-form content like feature articles can do wonders for growing sustainable businesses online — a strategy we wholeheartedly advocate at Content Hacker!

Writing a great feature article requires a combination of creativity, research, and effective storytelling. Here are some tips to help you craft a compelling feature story:

Build a Solid Narrative

Your feature article isn’t just sharing information; it’s telling a tale. With every line, you’re guiding readers on a journey that has them hanging onto every word until the very end.

A solid narrative arc is like a map through uncharted territory. It starts by setting up expectations in the beginning, building interest in the middle, and tying everything together at the end — a perfect circle of storytelling mastery.

Structure for Impact

We all know a good story grabs you from the start and sticks with you long after it’s done. The same goes for feature articles. When writing an engaging opening paragraph, think of it as your chance to invite readers into a conversation they’ll want to stick around for.

An outline shouldn’t be rigid but rather serve as guardrails keeping your thoughts aligned so that each section smoothly transitions into another without losing focus.

The structure of a feature article should feel natural — like listening to an old friend recounting an adventure.

Edit Like A Pro

Editing is where good writing becomes great, and a sharp editor’s eye can transform your feature article into a polished gem.

Crafting an article isn’t just about putting words on paper; it’s also about refining those words until they sing. The editing process demands that you scrutinize each sentence for grammar and spelling errors to present the most professional version of your work. Remember, even Pulitzer Prize winners revise their drafts — so should you.

A key stat to keep in mind: clear and coherent articles are more likely to hold the reader’s interest from start to finish. When revising, read aloud to catch any awkward phrasing or inconsistencies that could disrupt the flow.

While spellcheck helps, there’s no substitute for thorough proofreading. Typos can undermine credibility faster than factual inaccuracies. Take the time you need — every error you catch now is one less hurdle for your readers later on.

Get Feedback

You’ve crafted sentences like a pro, but another set of eyes can offer new perspectives. Seeking feedback before finalizing your work allows you to see how others perceive what you’ve written.

Remember that the writing process doesn’t end when you put down the pen; it continues through editing and fine-tuning based on constructive criticism.

Mastering how to write a feature article means diving deep into human stories. It’s about painting vivid pictures and touching hearts. You’ve learned the craft of choosing angles that resonate, structuring narratives for impact, and bringing out your unique voice.

You start with curiosity, build on solid research, and weave in compelling interviews.

Then you edit with precision — every word matters.

Your story breathes life when it reflects real people’s experiences. And now you have the blueprint to make sure every piece keeps readers hooked till the last word.

If writing features was daunting before, let this be your turning point.

It’s time to build your blog empire.

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Anthony Cockerill

Anthony Cockerill

| Writing | The written word | Teaching English |

The indispensable guide to what makes a great feature story

The feature story is a potent and vital form of literary non-fiction. here, anthony cockerill charts its evolution through the years..

Of all the different ways to tell stories, the feature article is one of the most compelling, especially when it’s in the right hands. It’s a mainstay of contemporary journalism: a set-piece at the core of a periodical amidst the recurring content, opinion columns and advertisements. A good feature story is authentic, without artifice or illusion, even though it can be as immersive as any great novel.

The feature article has more in common with the essay than traditional reportage, but unlike most essays, it is more akin to narrative. It makes productive use of story-telling strategies usually found in fiction. It is grounded in places and people. It offers an in-depth exploration. It’s a useful vehicle for the investigative journalist, but not all features are necessarily investigative in nature — a feature story might profile a noted person, or it might find a particular angle which illuminates a bigger issue. Like all great stories, a great feature exploits the reader’s pleasure in delayed gratification, as they engage willingly in the pleasure of being the passive participant of the narrative.

what part of speech is feature article


The essay is perhaps the earliest antecedent for the feature article, but the essay resists easy definition. Most people associate the form with the academic assignment — a means of assessing someone’s understanding of their studies — or perhaps the scholarly essay, published in disciplinary journals. But an essay — exploratory in both purpose and tone — can be critical, persuasive or personal in scope and all of these have influenced the evolution of the feature article.

When we read accomplished essayists such as Michel de Montagine, George Orwell and Clive James, we’re aware of a strong sense of subjectivity and enquiry. The essay writing process goes hand in hand with the process of developing thinking, which surely reflects the emergence of the form — the essais — as a way ascertaining and articulating opinion in an age when editing and redrafting was more difficult.

If the essay was a literary forebear, the advent of printing took the form to the masses. Printing by mechanical, moveable type spread knowledge and ideas in forms such as the tract, the pamphlet and in time, the newspaper and increased the franchise of literacy throughout Europe.

The daily newspaper is the taproot of modern journalism. Dailies mainly date to the eighteen-thirties, the decade in which the word ‘journalism’ was coined, meaning daily reporting, the  jour  in journalism. Jill Lepour, ‘Does Journalism Have A Future?’, The New Yorker

The Daily Courant, edited by Elizabeth Mallet, was Britain’s first daily newspaper, first published in 1702. Mallet claimed to provide only facts, to let the reader make up their own minds about events, demanding her authors ‘…relate only matter of fact; supposing other people to have sense enough to make reflections for themselves.’ This approach characterised news reportage throughout the 17th century, when contributions to newspapers were largely supplied by correspondents.

For the 18th century, it is possible to speak of a ‘literary’ journalism… the news was (no longer) at the centre of their activity, but rather its incorporation into larger narratives or extensive arguments. Jürgen Wilk e, Professor of Journalism, Johannes Gutenberg University

Jürgen Wilke, Professor of Journalism at Johannes Gutenberg University, has argued that the emergence of opinion journalism — the point where essay meets newspaper article — occurred in the 18th century. Newspapers became what he calls ‘organs of public opinion’. Wilke attributes this change to the failure of the Commons to renew the Printing Act in 1695, which had a direct impact on the freedom of the press. At this time ‘…the printer was responsible for the news… whereas the authors themselves oversaw the essay section and other sundry contributions. They… could also publish critical articles and voice their own opinion.’ As well as the daily newspaper, the period saw the gestation of the magazine — a monthly digest of news, review and commentary for the educated public. Although by no means the first, The Gentleman’s Magazine , first published in London in 1731, was the first periodical to use the enduring generic term. The ‘magazine’ evoked the idea of the armoury: a storehouse of powerful ideas and knowledge.

what part of speech is feature article

‘Many authors in the 18th century tried to gain a foothold in the booming sector of journal publishing,’ says Professor Wilke, ‘often writ[ing] other types of journalistic articles, for instance, essays and literary contributions to weeklies, which described themselves in their titles as journals.’

In the United Kingdom, compulsory education and the expansion of the electoral franchise in the 19th century led to growing literacy amongst the population. The repeal of the stamp tax in 1855 and the advent of the rotary press, combined with the availability of cheaper paper, facilitated a huge growth in the popularity and reach of both newspapers and magazines. Typical of the newly popular mass circulation magazine was Tit-Bits , published by George Newness, a miscellany of material gathered from a variety of sources and short fiction. The 19th century was also the age when scholarly journals expanded and the critical review emerged, in forms such as The Edinburgh Review and Quarterly Review . Charles Lamb’s  Essays  first appeared in The London Magazine , which was first published in 1820.

The content of periodicals at this time was essentially a combination of essay and exposition. A scholarly tone dominated and there was little sense of narrative. The editorial voice of The Spectator , founded in 1828, was expressed using the third-person personal pronoun ‘we’, implying an authority and collective understanding in line with the Enlightenment values of the time. This was evident as late as 1903: ‘We note with no little satisfaction that the feeling against Mr. Chamberlain’s proposals for taxing the food of the people is increasing every day.’ But by the following decade, the third-person pronoun — the editorial ‘we’ — had fallen out of style.

Casting an approving eye across the Atlantic in 1886, The Spector noted that Harper’s Monthly Magazine , established in 1850, ‘continue[d] to be worthy of [its] high reputation. Mr. Blackmore’s new story, ‘Springhaven,’ which… depicts the England that successfully resisted the first Napoleon, promises to be as good as anything that has recently come from the same pen. Under the title of ‘Their Pilgrimage,’ Mr. Dudley Warner gives a very lively account, slightly tinged, perhaps, with caricature, of American summer jauntings. ‘The New York Exchange’… tak[es] us behind the scenes of commercial life on the other side of the Atlantic for which this magazine is noted.’

what part of speech is feature article

First edited by James Russell Lowell, The Atlantic Monthly was founded in 1857. ‘Our Birds, And Their Ways’, published during that inaugural year, gives an interesting account of the habits of birds native to America. ‘Among our summer birds,’ begins the article, ‘the vast majority are but transient visitors, born and bred far to the northward and returning thither every year.’ The article occasionally makes use of the first-person style (‘I have seen crows in the neighbourhood of Boston every week of the year…’) and on occasion, direct speech (‘My friend the ornithologist said to me last winter, “You will see that they will be off as soon as the ground is well covered in snow…”‘) But these stylistic choices are rare. There is very little to discern by way of the influence of fiction.

Investigative journalism and social commentary

If the rule of the 19th century periodical feature was scholarly exposition, one notable exception was Charles Dickens. Charles Dickens’ own journalism is notable for blending, stylistically at least, fiction and non-fiction. The critic Michael Dirda has written that while ‘there is a good deal of fancy in Dickens’ reportage, the second half of Sketches by ‘Boz’ consists of what are, in fact, out-and-out short stories.’ In these vignettes, Dickens created a particularly literary form of journalism which gave him an opportunity to craft the characteristic social commentary which was, of course, was similarly conspicuous in his fiction. This wasn’t to everyone’s taste. In 1853, The Spectator published a scathing review of Dickens’ Bleak House , accusing him of ‘amusing the idle hours of the greatest number of readers; not, we may hope, without improvement to their hearts, but certainly without profoundly affecting their intellects or deeply stirring their emotions.’ This scathing indictment perhaps illustrates the division between the intellectualism of the established society periodicals and the nascent ‘populism’ emerging at the time, pioneered in British journalism by William Thomas Stead.

As the editor of The Pall Mall Gazette , editor and social reformer Stead paved the way for the investigative journalism which remains, to some degree, part of the feature article as we know it: eye-catching headlines, subheadings and visuals. Even more importantly, Stead influenced the tropes of this journalism, pioneering the newspaper interview and the subjective presence of the writer within the text, such as in his reportage of the famous Eliza Armstrong case, an important example of a journalist creating news to write about, rather than merely reporting events. In the USA, the sensationalism of Stead was paralleled in the journalism published by William Randolph Hearst, owner of the New York Journal , and Joseph Pulitzer, owner of the New York World . These developments prompted a visceral response from some critics, notably Matthew Arnold, who pejoratively called Stead’s output ‘New Journalism’. This was more than simple grumbling: the emergence of the popular press instigated a debate about the value of journalistic objectivity.

what part of speech is feature article

Adolph Ochs, who bought the New York Times in 1896, must have been a man from the same school of thought as Matthew Arnold: he banned comic strips and gossip columns from the newspaper, and in doing so, focused the publication’s efforts on objective journalism, raising the profile of the newspaper and establishing its international reputation. That same year, The New York Times Magazine was first printed under the auspices of Ochs, establishing the magazine as an outlet for photo-journalism and features.

People and their stories

At a time when Freud was developing his theories of the unconscious and painters like Picasso were experimenting with Cubism, journalists were also developing a greater recognition of human subjectivity. Walter Dean, American Press Institute

Despite inroads into a more literary style of journalism made by the likes of Dickens, as a whole, literary influences in the form continued to feel restrained. The tone of ‘Golf’, a feature in The Atlantic Monthly in 1902, feels predominantly like an essay rather than a story. Although there is a strong sense of authorship and a slight sense of irony (‘Empire, trusts, and golf — these are the new things in American life…’) and although structured like an essay that ranges around its subject, narrative is largely absent.

Writing in Nordicom Review on the featurisation of journalism, Steen Steensen, Professor of Journalism at Oslo Metropolitan University, has argued that the feature article as we understand it in its modern form is a creation of the 20th century. It certainly seems to be the early years of the 1900s in which we can begin to discern an approach to the feature article that emphasises people and their experiences. The Atlantic Monthly printed a polemical essay about animal experimentation from John Dewey in September 1926. ‘In Jerusalem a great Jewish university is being slowly developed,’ wrote Henry W Nevison in the same publication in May 1927. There is a sense that the stories and the pursuits of people were beginning to take centre stage.

National Geographic Magazine was a scholarly journal until 1905, when it became known for what it continues to do well — extensive pictorial content and photojournalism — under the editorial control of Gilbert H Grosvenor. In 1905, the magazine published a feature article called ‘The Purple Veil’, subtitled ‘A Romance of the Sea.’ Clearly, there are the beginnings of a narrative approach. The ‘purple veil’ of the title, as the article later reveals, is the egg mass of Lophius piscatorius , or the goose-fish. ‘Off the New England coast,’ begins the article, ‘a curious object is often found floating on the water, somewhat resembling a lady’s veil of gigantic size and of a violet or purple colour. The fishermen allude to it generally as “the purple veil,” and many have been the speculations concerning its nature and origin.’ There is a pleasing sense of immersion, a sort of ‘cold open’ that is unabashedly designed to hook the reader.

what part of speech is feature article

In ‘The Date Gardens of the Jerid’, written by Thomas H Kearney and published in National Geographic Magazine in 1910, the author begins with the immersive, sense of place opening that the magazine is known for: ‘With its feet in the water and its head in the fire, as the Arab proverb has it, the date palm is at home in the vast deserts that stretch from Morocco to the borders of India.’ After this initial scene setting, the author segues into exposition, telling us: ‘Some years ago, I visited these oases in order to obtain palms for the date orchards which the National Department of Agriculture has established in Arizona and in the Colorado Desert of California.’ This juxtaposition of vibrant image and elucidation continues to be a key structural technique in feature stories today.

Brazenly literary in style, lyrically crafted and undoubtedly novelistic, Florence Craig Albrecht begins ‘Channel Ports – And Some Others’ in 1915 by describing a maritime voyage:

‘The sturdy old vessel is coming into port after an eventless voyage. Seven days of ceaseless plowing through a shimmering sea, under a great round dome, now radiant light, now dusky velvet, star-sprinkled. The Scillys have floated by, foam-washed, mist wrapped, fairly islands in a magic world all cloud and water.’ ‘Channel Ports – And Some Others’, Florence Craig Albrecht

Albrecht has embraced a literary narrative and established a strong sense of place. There is also a clear omniscient point-of-view at work here that evokes the establishing shot of a film in style. This embrace of immediacy in the story-telling — and of movement — feels very much inspired by moving image.

Morris Markey wrote ‘Gangs’ in The Atlantic Monthly in March 1928. Again, the sense of people and place is palpable:

‘On a pleasant evening, not many weeks ago,’ writes Markey in his opening paragraph, ‘a young man bearing the rather picturesque name of Little Augie was standing with a friend on the street corner in New York’s lower East Side. The friend was facing toward the curb, and suddenly, he gave a cry of warning. Little Augie swung about in time to see an automobile charge down upon him.’ ‘Gangs’, Morris Markey

From this evocative opening scene, Markey goes on to explore the backstory; to fill in some of the detail behind Little Augie’s death. The writing is composed of scenes and exposition which are woven together. Furthermore, these scenes and expository components are structured together in longer narrative sequences, divided by Roman numerals. Clearly, there is an emerging sense of the fictional form and its associated stylistics exerting a strong influence in the composition of the text.

Just as Dickens’ journalism had been characterised by aspects of narrative befitting a novelist, in the early decades of the 20th Century, Ernest Hemingway also began his writing career in the newsroom. Hemingway’s experiences writing journalism famously influenced his fiction. Taking the nod from the style guide at the Kansas City Star , where he worked as a reporter after leaving high school, his fiction became known for its objective narrative perspective and lucid sentences. Conversely, Hemingway’s reportage had always been literary. It had sketches, descriptions, characters and a sense of narrative which set the inverted pyramid of the news story the right way up. In his article ‘At the End of the Ambulance Run’, a newspaper article for the  Kansas City Star from 1918, Hemingway begins his copy with the ominous action of a short story:

The night ambulance attendants shuffled down the long, dark corridors at the General Hospital with an inert burden on the stretcher. They turned in at the receiving ward and lifted the unconscious man to the operating table. His hands were calloused and he was unkempt and ragged, a victim of a street brawl near the city market. No one knew who he was, but a receipt, bearing the name of George Anderson, for $10 paid on a home out in a little Nebraska town served to identify him. ‘At the End of the Ambulance Run’, Ernest Hemingway

This is an approach to storytelling usually found in fiction, where the writer lures the reader, leaves them to work out what is or isn’t fundamentally crucial, then builds to a climax. It is essentially the structural and stylistic opposite of the classic inverted pyramid, which condenses news, summarises and foregrounds the most important part of the story.

In 1933, by this time established as a writer of fiction, Hemingway was made an offer he couldn’t refuse by Arnold Gingrich, who had founded Esquire that same year. ‘[He sent Hemingway] a blue sports shirt and a leather jacket, promising to pay him $250 each for articles about marlin-fishing in Cuba, lion-shooting in Tanganyika, bullfighting in Spain, and other manly subjects,’ said Carlos Baker, writing in The New York Times in 1967.

Hemingway has been seen as a profound influence on what was to be called ‘New Journalism’, and although he was undoubtedly a totemic figure, something was happening that was bigger than one person: an undercurrent of fictional stylistics gathering strength in literary journalism, the stylistics of which itself was influenced by cinema, as is evident in Stewart H Holbrook’s ‘Life of a Pullman Porter’ , published in Esquire in 1939:

One October evening in 1937 a stunning blonde of about thirty took a Pullman compartment on a Great Northern train leaving Portland, Oregon for Seattle. She was a tall, graceful woman, modishly dressed in dark blue, right up to her earrings, and the porter who was on her car still thinks she was the handsomest woman he has ever seen. An hour or so later, as the train was leaving Longview, Washington, the lady rang for the porter and handed him a letter in a pale blue envelope. “I want you to be sure,” she said with emphasis, “to mail this at Aberdeen and nowhere else.” She gave him a quarter Stewart H Holbrook, ‘Life of a Pullman Porter’

Alongside feature journalism, in the 1930s Esquire ran short-stories in abundance, as well as ‘semi-fiction’ – an interesting idea that involved real stories, fictionalised for publication. The influence of moving image in writing in this period can be felt palpably. The introduction to Laura Marcus’s essay, ‘Cinema and Modernism’, notes that modernism was ‘concerned with everyday life, perception, time and the kaleidoscopic and fractured experience of urban space. Cinema, with its techniques of close-up, panning, flashbacks and montage played a major role in shaping experimental works.’ As authors of fiction embraced the possibilities of the new medium, their stylistic influences were felt keenly in the work of feature journalists of the era.

‘Movies were already by then a part of the culture… motion was a part of the new vocabulary… for the first time in conventional reporting people began to move. They had a journalistic existence on either side of the event,’ wrote Michael J Arlen in The Atlantic Monthly in 1972. It is impossible to overestimate the influence of the cinema on literary non-fiction, just as the cinema profoundly influenced Modernist fiction during those years.

The influence of ‘New Journalism’

‘ Joe Louis at Fifty’ wasn’t like a magazine article at all. It was like a short story. It began with a scene, an intimate confrontation between Louis and his third wife. Tom Wolfe, Bulletin of the American Society Newspaper Editors , 1970

Matthew Arnold had disparagingly used the phrase ‘New Journalism’ to describe the evolving journalism of the 19th Century that was characterised by a different more sensational discourse and subject matter. In the 1960s, the American journalist Tom Wolfe used the appellation to describe his perception of a shift in the style and framing of the journalism of a cluster of writers in the period who would work in a much more literary style, espousing ‘truth’ over ‘facts’. Unlike Arnold, however, Wolfe certainly wasn’t being disparaging. In fact, there was an element of braggadocio at play. ‘New Journalism’ in Wolfe’s opinion was revolutionary, and Wolfe was one of its biggest proponents.

‘Wolfe wrote that his first acquaintance with a new style of reporting came in a 1962 Esquire article about Joe Louis by Gay Talese,’ wrote James E Murphy in The New Journalism: A Critical Perspective . For Wolfe, Talese was the first to apply fiction techniques to his reporting. But Dickens had done so, as had Hemingway and many others. Experimenting with personal narrative and the blurring of fact and fiction was hardly new. If we were to take Tom Wolfe to task for over-egging the contribution of the New Journalists, we wouldn’t be the first. To argue that New Journalism isn’t exactly new is what Michael J Arlen has called ‘a favourite put down’. In The Atlantic in 1972, he argued that ‘there’s been a vein of personal journalism in English and American writing for a very long time.’

I wonder if what happened wasn’t more like this… that despite the periodic appearance of an Addison, or Defoe, or Twain, standard newspaper journalism remained a considerably restricted branch of writing, both in England and America, well into the nineteen twenties… then, after the First World War, especially the literary resurgence in the nineteen twenties — the  writers’  world of Paris, Hemingway, Fitzgerald, etc. — into the relatively straitlaced, rectilinear, dutiful world of conventional journalism appeared an assortment of young men who wanted to do it differently.… Michael J Arlen, ‘Notes on the New Journalism’, The Atlantic Monthly , May 1972

Once Tom Wolfe graduated, argues Arlen, ‘burdened like the rest of his generation with the obligation to write a novel’, he made an important discovery: ‘the time of the novel was past… [a] fairly profound change was already taking place in the nation’s reading habits… most magazines, which had been preponderantly devoted to fiction, were now increasingly devoted to nonfiction.’

what part of speech is feature article

Gay Talese’s article, ‘Frank Sinatra Has A Cold’, published in Esquire in April 1966, is, quite rightfully, a staple of journalism students’ reading lists. Denied the opportunity to talk to Sinatra, Esquire editor Harold T. P. Hayes nevertheless kept Talese on the job. Talese ‘bounc[ed] from hope to despair to paranoia and back as he work[ed] furiously to deliver the goods by shadowing the notoriously controlling Sinatra and talking to everyone who might be able to shed light on the entertainer without setting off any alarms,’ wrote Frank Digiacomo in Vanity Fair in 2006. The distance between Talese and Sinatra became the story itself, and offered Talese an angle on Sinatra’s volatile temper and fragile ego. Scenes from Talese’s time observing Sinatra are adroitly rendered:

Frank Sinatra, leaning against the stool, sniffling a bit from his cold, could not take his eyes off the Game Warden boots. Once, after gazing at them for a few moments, he turned away; but now he was focused on them again. The owner of the boots, who was just standing in them watching the pool game, was named Harlan Ellison, a writer who had just completed work on a screenplay,  The Oscar. Finally Sinatra could not contain himself. ‘Hey,’ he yelled in his slightly harsh voice that still had a soft, sharp edge. ‘Those Italian boots?’ ‘No,’ Ellison said. ‘Spanish?’ ‘No.’ ‘Are they English boots?’ ‘Look, I donno, man,’ Ellison shot back, frowning at Sinatra, then turning away again. Gay Talese, ‘Frank Sinatra Has a Cold’

Stylistics of contemporary feature stories

…feature journalism is best understood as a family of genres that has traditionally shared a set of discourses: a literary discourse, a discourse of intimacy and a discourse of adventure. Steen Steensen, Professor of Journalism, Oslo Metropolitan University

The ubiquity of the overtly literary feature article and the associated stylistic choices has waned since the New Journalists’ heyday. Today’s feature stories feel less self-consciously ‘fictional’ than some of those New Journalism classics. They are lighter on direct speech and tend toward more reported speech. Dialogue tags are usually in present tense, which conveys immediacy but which loses some of the fictional notes that resonate soundly in Gay Talese’s writing. Despite this, the methods of story-telling associated with New Journalism continues to exert a powerful influence on the feature story, especially in terms of narrative structure and style.

The scene is at the heart of the feature story, and these scenes are clustered into sequences, a legacy of the influence of cinema. We can see the legacy of this in the discourse of contemporary feature writing: the narrative structure, the sequences of scenes, the evocation of people and places, the importance of the story in moving the writing forward.

Lee Gutkind has explored the structure of the feature story in detail in his 2012 book You Can’t Make This Stuff Up . The structure of the feature story builds to a sense of climax — this could perhaps be a revelatory moment of insight. Gutkind makes the case for the importance of the scene ‘to communicate ideas and information as compellingly as possible,’ to keep the reader engaged through powerful story-telling, around which exposition can be arranged.

Writing for GQ , Jonathan Heaf begins his profile of Harrison Ford prior to the release of Star Wars: The Force Awakens , with a brutal, in-media-res opening paragraph:

‘I don’t want all this to take all  f * ing  day.’ The words Harrison Ford, 73, not so much spoke as snarled at me yesterday afternoon while discussing our lunch plans are still, 24 hours later, smarting like refined sugar hitting an exposed tooth cavity. Jonathan Heaf, ‘Harrison Ford on his change of heart about Han Solo’, GQ , January 2015

This appears to confirm some uncomfortable truths about Ford that Heaf — and by extension, we as readers — might have expected: Ford is invariably grumpy; Ford is an ungenerous interviewee; Ford is a formidable challenge. But by the end of the profile, having woven the narrative (his meeting with Ford, a ride in his Tesla Model S, lunch) with interview and exposition, Heaf describes telling Ford how much he’s looking forward to watching Star Wars: The Force Awakens :

He smiles. I turn to walk back to my rental and that’s when I hear it. “Me too, kid.” I very nearly glance back. Kid. He called me kid. For the first time all day, a flicker. One word, that’s all I needed. That ‘kid’, uttered in that tone, in his voice, fires my memory banks like a proton torpedo fired into a thermal exhaust port. Jonathan Heaf, ‘Harrison Ford on his change of heart about Han Solo’, GQ , January 2015

The centrality of the writer creates a kinship with a reader who has grown up with the same cultural tropes and shared, mimetic reference points. The structure of the writing allows Heaf to demonstrate the journey toward intimacy alluded to by Steensen. The Observer Magazine publishes around three features a week. Some are essentially pieces of investigative journalism into topics such as healing crystals (‘The New Stone Age’ by Eva Wiseman, June 2019) and Britain’s big cats, (‘Here, Kitty?’ by Mark Wilding, April 2019). Many, however, tell us something bigger about society in general: Eva Wiseman explores the well-being industry (‘Feel Better Now?’, March 2019). Joanna Moorhead learns about art therapy in prisons (‘Brushes With The Law’, May 2019). Alex Moshakis probes the big business of house plants (‘The Bloom Economy’, June 2019).

Some recount authentic, personal experiences — of sexuality (‘What My Queer Journey Taught Me About Love’, Amelia Abraham, May 2019) or of racing pigeons (‘Home to Roost’, Jon Day, June 2019). Other stories are contrived, for example, Emma Beddington transforms her dog into an Instagram star (‘Meet The World’s Most Unlikely Insta Star’, July 2019). There are often profiles of notable individuals, such as comedian Sara Pascoe (‘I wanted To Be Prime Minister’, Rebecca Nicholson, August 2019) and crossword writer Anna Schectman (‘Why It’s Hip To Be Square’ by Alex Moshakis).

Some tell really interesting stories — the fashion historian who solves crimes (‘Call The Fashion Police’, Eva Wiseman, March 2019). Others are about trends: the television box set (‘Why Box Sets Suck Us In’ by Will Storr, April 2019) and the male wellness sector (‘The Evolution of Man’ by Alex Moshakis, March 2019). Within each of these stories is really engaging content, things we can identify with, references we can share with the writer. The feature article has an important role, bringing people’s stories into play to lead us toward bigger truths and illuminating aspects of our culture and society.

If these features adopt Steensen’s ‘discourse of intimacy’, perhaps the travel feature best exemplifies the ‘discourse of adventure’. The writer Dan Richards, who specialises in travel and adventure, visited Finland’s Pellinge archipelago for 1843 (December 2019/January 2020) to explore the landscape that inspired Tove Jansson, author of the Moomin children’s books. Richards writes compellingly about the outdoors (his contribution to Holloway , which he co-authored with Robert Macfarlane, is lucid and lyrical) and here, he evokes the sparse beauty of the archipelago in his search for the places where Jansson lived and worked. The notion of a ‘search’, a sense of discovery at the heart of the narrative, is a crucial element of the ‘discourse of adventure’, even if for Richards, the island which inspired the Sommarboken [The Summer Book] remains elusive. Just as with Gay Talese’s search for Sinatra, here the adventure is the momentum of the narrative, even if the sought-after moment must be deferred.

Where are we now?

The turn of the twentieth century was marked by one of the most important cultural thresholds in society: the advent of the motion picture. Michael J Arlen is surely right when he argues that this was the significant moment when the feature article began to take on a sense of movement; when showing, rather than telling, came to the fore. It was only natural that writers should turn to fictional narrative as the toolkit. The importance of human subjectivity as central to aesthetic experience was a profound sea change that reflected wider socio-cultural changes: the decline of trust in authority, the erosion of the Enlightenment meta-narrative.

The essay, that long-established form of literary non-fiction, remains a crucial, vibrant form in its own right that can often be found in the package of the published ‘feature’. But the codes and conventions of narrative story-telling have become synonymous with the feature story, what Lee Gutkind prefers to call ‘creative non-fiction’.

Just as the influence of cinema was keenly felt, no doubt the influence of the web and the convergent device will begin to influence the adaptation of the feature story. The modus operandi of the ‘Mojo’ — the mobile journalist — asks the question of what the role of the feature article in today’s fast paced world might be — and the extent to which it can compete with the immediacy of images, video and the flow of social media feeds.

what part of speech is feature article

But just because we like our news reportage raw doesn’t mean we’re turned off to the payload of a great story. Steen Steensen has argued that feature journalism is transforming traditional ‘hard news’ in a process he calls the ‘featurization of journalism’ — the increasing dominance of feature-style journalism in newspapers. This, he writes, is often viewed by academics as an erosion of the social function of the press, ‘divert[ing] journalism towards what might interest the public instead of what is in the public’s interest, hence weakening the role of the news media in a democracy’. However, Steensen goes on to argue that in fact, the traditional genres of hard news and feature journalism have become entwined to some degree, in terms of discourse and social function, bringing ‘enlightenment and insight into complex and quintessential matters of culture and society.’

Steensen goes on to caution that the general transformation of news into something more ‘consumer-oriented, intimate and fiction-inspired’ might create a conflict of ‘intentions and expectations’. This is a judicious caveat in our post-truth culture. The feature story continues to feel like an urgent, exciting and relevant way to tell the stories of the people and places in our world. It will continue to stand as an important social function if we adhere to Lee Gutkind’s injunction to ‘be true to your story, true to your characters, true to yourself.’

Further Reading

Gutkind, Lee (2012) You Can’t Make This Stuff Up, Boston: Da Capo Press.

Harrington, H.F. (1912) Essentials in Journalism: A Manual in Newspaper Making for College Classes , Boston: The Athenaeum Press.

Arlen, Michael J (1972) ‘Notes on the New Journalism’, The Atlantic Monthly .

Hellmann, John (1977) ‘Fables of Fact: New Journalism Reconsidered’, The Centennial Review Vol. 21, No. 4.

Lepore, Jill (2019) ‘Does Journalism Have A Future? The New Yorker .

Murphy, James E (1974) ‘The New Journalism: A Critical Perspective’, Journalism Monographs , No. 34.

Steensen, Steen (2011) ‘The Featurization of Journalism’, Nordicom Review .

Wilke, Jürgen (1987) ‘Newspapers and their reporting – a long-term international comparison’ in Deutsche Presseforschung Bremen [German Press Research Bremen] (ed.): Presse und Geschichte [The press and its history], Munich 1987, vol. 2: Neue Beiträge zur historischen Kommunikationsforschung [New contributions to research on historical communication].

Wolfe, Tom (1970) ‘The New Journalism’, Bulletin [of the American Society of Newspaper Editors].

Dean, Walter ‘The lost meaning of “objectivity”‘, American Press Institute.

Marcus, Laura (2016) ‘Cinema and modernism’, The British Library.

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How to Write a Feature Article: A Step-by-Step Guide

Feature stories are one of the most crucial forms of writing these days, we can find feature articles and examples in many news websites, blog websites, etc.  While writing a feature article a lot of things should be kept in mind as well. Feature stories are a powerful form of journalism, allowing writers to delve deeper into subjects and explore the human element behind the headlines. Whether you’re a budding journalist or an aspiring storyteller, mastering the art of feature story writing is essential for engaging your readers and conveying meaningful narratives. In this blog, you’ll find the process of writing a feature article, feature article writing tips, feature article elements, etc. The process of writing a compelling feature story, offering valuable tips, real-world examples, and a solid structure to help you craft stories that captivate and resonate with your audience.

Read Also: Top 5 Strategies for Long-Term Success in Journalism Careers

Table of Contents

Understanding the Essence of a Feature Story

Before we dive into the practical aspects, let’s clarify what a feature story is and what sets it apart from news reporting. While news articles focus on delivering facts and information concisely, feature stories are all about storytelling. They go beyond the “who, what, when, where, and why” to explore the “how” and “why” in depth. Feature stories aim to engage readers emotionally, making them care about the subject, and often, they offer a unique perspective or angle on a topic.

Tips and tricks for writing a Feature article

 In the beginning, many people can find difficulty in writing a feature, but here we have especially discussed some special tips and tricks for writing a feature article. So here are some Feature article writing tips and tricks: –

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1. Choose an Interesting Angle:

The first step in feature story writing is selecting a unique and compelling angle or theme for your story. Look for an aspect of the topic that hasn’t been explored widely, or find a fresh perspective that can pique readers’ curiosity.

2. Conduct Thorough Research:

Solid research is the foundation of any feature story. Dive deep into your subject matter, interview relevant sources, and gather as much information as possible. Understand your subject inside out to present a comprehensive and accurate portrayal.

3. Humanize Your Story:

Feature stories often revolve around people, their experiences, and their emotions. Humanize your narrative by introducing relatable characters and sharing their stories, struggles, and triumphs.

4. Create a Strong Lead:

Your opening paragraph, or lead, should be attention-grabbing and set the tone for the entire story. Engage your readers from the start with an anecdote, a thought-provoking question, or a vivid description.

5. Structure Your Story:

Feature stories typically follow a narrative structure with a beginning, middle, and end. The beginning introduces the topic and engages the reader, the middle explores the depth of the subject, and the end provides closure or leaves readers with something to ponder.

6. Use Descriptive Language:

Paint a vivid picture with your words. Utilize descriptive language and sensory details to transport your readers into the world you’re depicting.

7. Incorporate Quotes and Anecdotes:

Quotes from interviews and anecdotes from your research can breathe life into your story. They add authenticity and provide insights from real people.

8. Engage Emotionally:

Feature stories should evoke emotions. Whether it’s empathy, curiosity, joy, or sadness, aim to connect with your readers on a personal level.

Read Also: The Ever-Evolving World Of Journalism: Unveiling Truths and Shaping Perspectives

Examples of Feature Stories

Here we are describing some of the feature articles examples which are as follows:-

“Finding Beauty Amidst Chaos: The Life of a Street Artist”

This feature story delves into the world of a street artist who uses urban decay as his canvas, turning neglected spaces into works of art. It explores his journey, motivations, and the impact of his art on the community.

“The Healing Power of Music: A Veteran’s Journey to Recovery”

This story follows a military veteran battling post-traumatic stress disorder and how his passion for music became a lifeline for healing. It intertwines personal anecdotes, interviews, and the therapeutic role of music.

“Wildlife Conservation Heroes: Rescuing Endangered Species, One Baby Animal at a Time”

In this feature story, readers are introduced to a group of dedicated individuals working tirelessly to rescue and rehabilitate endangered baby animals. It showcases their passion, challenges, and heartwarming success stories.

What should be the feature a Feature article structure?

Read Also: What is The Difference Between A Journalist and A Reporter?

Structure of a Feature Story

A well-structured feature story typically follows this format:

Headline: A catchy and concise title that captures the essence of the story. This is always written at the top of the story.

Lead: A captivating opening paragraph that hooks the reader. The first 3 sentences of any story that explains 5sW & 1H are known as lead.

Introduction : Provides context and introduces the subject. Lead is also a part of the introduction itself.

Body : The main narrative section that explores the topic in depth, including interviews, anecdotes, and background information.

Conclusion: Wraps up the story, offers insights, or leaves the reader with something to ponder.

Additional Information: This may include additional resources, author information, or references.

Read Also: Benefits and Jobs After a MAJMC Degree

Writing a feature article is a blend of journalistic skills and storytelling artistry. By choosing a compelling angle, conducting thorough research, and structuring your story effectively, you can create feature stories that captivate and resonate with your readers. AAFT also provides many courses related to journalism and mass communication which grooms a person to write new articles, and news and learn new skills as well. Remember that practice is key to honing your feature story writing skills, so don’t be discouraged if it takes time to perfect your craft. With dedication and creativity, you’ll be able to craft feature stories that leave a lasting impact on your audience.

What are the characteristics of a good feature article?

A good feature article is well-written, engaging, and informative. It should tell a story that is interesting to the reader and that sheds light on an important issue.

Why is it important to write feature articles?

Feature articles can inform and entertain readers. They can also help to shed light on important issues and to promote understanding and empathy.

What are the challenges of writing a feature article?

The challenges of writing a feature article can vary depending on the topic and the audience. However, some common challenges include finding a good angle for the story, gathering accurate information, and writing in a clear and concise style.


Aaditya Kanchan is a skilled Content Writer and Digital Marketer with experience of 5+ years and a focus on diverse subjects and content like Journalism, Digital Marketing, Law and sports etc. He also has a special interest in photography, videography, and retention marketing. Aaditya writes in simple language where complex information can be delivered to the audience in a creative way.

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what part of speech is feature article

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  • the car down the street, the man next to you
  • a book, an apple, a bottle
  • the definite article the : You use it before a singular or a plural noun when you talk about one or more specific member(s) of a group (things, places or people) that is known to you: the tall man, the big house, the man next to me ;
  • the indefinite articles a/an : You use them before a singular noun when you talk about any general thing : a line, a house, a kitchen, a person, an apple, an airport, an idea, an umbrella .
  • You use the article a before nouns/adjectives or numbers that start with a consonant : a line, a kitchen, a person, a dog, a book, a tall man, a five-year-old boy, a job interview .
  • You use the article an before nouns that start with a vowel : an apple, an idea, an umbrella, an egg, an hour, an eight-year-old girl, an interview .
  • There is --- a an airport close to the city.  
  • Do you have --- a an armchair in your room?  
  • She has --- an a idea!  
  • They have --- an a female English teacher.  
  • He eats --- an a apple.  
  • There is --- a an school around the corner.  
  • She has --- an a new armchair.  
  • We will give him --- a an book for his birthday.  
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  • He works as pilot.  
  • I need new TV.  
  • He is best teacher at the school.  
  • They have eight-year-old girl.  
  • book she bought yesterday is not so good.  
  • She is nicest girl I know.  
  • She is nice girl.  
  • city that she likes the most is New York City.  
  • time  
  • example  
  • adjective  
  • week  
  • door  
  • elephant  
  • bike  
  • shop  
  • number  
  • umbrella  
  • opinion  
  • English book  
  • table  
  • eagle  
  • Michael says: "I have best friend. His name is Josh. He lives in small house outside the city. They have beautiful garden behind house. house is painted blue and there is fence around garden. I love going there. It's so nice and peaceful."  

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what part of speech is feature article


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How to write a Feature Article

what part of speech is feature article

Need to write a Feature Article for a magazine or newspaper but not sure where to start?

This guide includes links to tools and resources that can help you.

What is a Feature Article?

what part of speech is feature article

A  feature article is an article written to dive deeper into a story than a regular article and give more depth to topical events, people or issues.

Feature articles are written by an expert or a journalist. They provide background information on a newsworthy topic as well as the writer's personal slant or experience.

You will find feature articles in newspapers, magazines and online.

Structure of a Feature Article

A well-written feature article  follows a specific format and will include the following:

Other elements you may wish to include in your Feature Article:

  • Images / Photos / Illustrations
  • Columns - split text into two or three columns
  • Break-out boxes - boxes which separate text from the main part of the story

F eature Article Example

what part of speech is feature article

A Pocketful of Pennies. Teen Breathe , Issue 13, 10-13.

Tools to create a Feature Article

Use the following tools and resources to help you format your Feature article.

what part of speech is feature article

Templates  for magazine articles by Canva

This video shows you how to use Google Docs to format a magazine-style article. 

Run Time: 5:17 mins

This video includes tips and a guide to writing and "designing" a great feature article

Run Time: 4:00 mins

More tips for writing a newspaper or feature article

Image credits

Johnson, L. (2017, February 3).  How to Format Google Document as Magazine Style Article  [Video]. Youtube.

mskrieger . (n.d.).  Smart tips for writing a great feature article   [Video]. Youtube.

A Pocketful of Pennies.  Teen Breathe , Issue 13, 10-13.

Scholastic. (n.d.). Writing a Newspaper Article.

Cover image credits

Angelova, N. (2019, April 4). [Photograph of a magazine on a bed] [Photograph]. Unsplash.

Kenion, C. (2018, January 17). [Photograph of a magazine display][Photograph]. Unsplash.

Robertson, E. (2017, February 15). [Photograph of a magazine and glasses] [Photograph]. Unsplash.

Thought Catalog. (2018, March 2). Fashion magazine [Phoograph]. Unsplash.

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Parts of Speech

What are the parts of speech, a formal definition.

Table of Contents

The Part of Speech Is Determined by the Word's Function

Are there 8 or 9 parts of speech, the nine parts of speech, (1) adjective, (3) conjunction, (4) determiner, (5) interjection, (7) preposition, (8) pronoun, why the parts of speech are important, video lesson.

parts of speech

  • You need to dig a well . (noun)
  • You look well . (adjective)
  • You dance well . (adverb)
  • Well , I agree. (interjection)
  • My eyes will well up. (verb)
  • red, happy, enormous
  • Ask the boy in the red jumper.
  • I live in a happy place.
  • I caught a fish this morning! I mean an enormous one.
  • happily, loosely, often
  • They skipped happily to the counter.
  • Tie the knot loosely so they can escape.
  • I often walk to work.
  • It is an intriguingly magic setting.
  • He plays the piano extremely well.
  • and, or, but
  • it is a large and important city.
  • Shall we run to the hills or hide in the bushes?
  • I know you are lying, but I cannot prove it.
  • my, those, two, many
  • My dog is fine with those cats.
  • There are two dogs but many cats.
  • ouch, oops, eek
  • Ouch , that hurt.
  • Oops , it's broken.
  • Eek! A mouse just ran past my foot!
  • leader, town, apple
  • Take me to your leader .
  • I will see you in town later.
  • An apple fell on his head .
  • in, near, on, with
  • Sarah is hiding in the box.
  • I live near the train station.
  • Put your hands on your head.
  • She yelled with enthusiasm.
  • she, we, they, that
  • Joanne is smart. She is also funny.
  • Our team has studied the evidence. We know the truth.
  • Jack and Jill went up the hill, but they never returned.
  • That is clever!
  • work, be, write, exist
  • Tony works down the pit now. He was unemployed.
  • I will write a song for you.
  • I think aliens exist .

Are you a visual learner? Do you prefer video to text? Here is a list of all our grammar videos .

Video for Each Part of Speech

what part of speech is feature article

The Most Important Writing Issues

The top issue related to adjectives, the top issue related to adverbs.

  • Extremely annoyed, she stared menacingly at her rival.
  • Infuriated, she glared at her rival.

The Top Issue Related to Conjunctions

correct tick

  • Burger, Fries, and a shake
  • Fish, chips and peas

The Top Issue Related to Determiners

wrong cross

The Top Issue Related to Interjections

The top issue related to nouns, the top issue related to prepositions, the top issue related to pronouns, the top issue related to verbs.

  • Crack the parts of speech to help with learning a foreign language or to take your writing to the next level.

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Literacy Ideas

Parts of Speech: The Ultimate Guide for Students and Teachers

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This article is part of the ultimate guide to language for teachers and students. Click the buttons below to view these.

What are Parts of Speech ?

Just as a skilled bricklayer must get to grips with the trowel, brick hammer, tape measure, and spirit level, the student-writer must develop a thorough understanding of the tools of their trade too.

In English, words can be categorized according to their common syntactic function in a sentence, i.e. the job they perform.

We call these different categories Parts of Speech . Understanding the various parts of speech and how they work has several compelling benefits for our students.

Without first acquiring a firm grasp of the various parts of speech, students will struggle to fully comprehend how language works. This is essential not only for the development of their reading comprehension but their writing skills too.

Visual Writing

Parts of speech are the core building blocks of grammar . To understand how a language works at a sentence and a whole-text level, we must first master parts of speech.

In English, we can identify eight of these individual parts of speech, and these will provide the focus for our Complete Guide to Parts of Speech .

THE EIGHT PARTS OF SPEECH (Click to jump to each section)

A complete unit on teaching figurative language.

Parts of Speech | figurative language Unit 1 | Parts of Speech: The Ultimate Guide for Students and Teachers |

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parts of speech, what is a noun?

Often the first word a child speaks will be a noun, for example, Mum , Dad , cow , dog , etc.

Nouns are naming words, and, as most school kids can recite, they are the names of people, places, and things . But, what isn’t as widely understood by many of our students is that nouns can be further classified into more specific categories. 

These categories are:

Common Nouns

Proper nouns, concrete nouns, abstract nouns, collective nouns, countable nouns, uncountable nouns.

All nouns can be classified as either common or proper .

Common nouns are the general names of people, places, and things. They are groups or classes on their own, rather than specific types of people, places, or things such as we find in proper nouns.

Common nouns can be further classified as abstract or concrete – more on this shortly!

Some examples of common nouns include:

People: teacher, author, engineer, artist, singer.

Places: country, city, town, house, garden.

Things: language, trophy, magazine, movie, book.

Proper nouns are the specific names for people, places, and things. Unlike common nouns, which are always lowercase, proper nouns are capitalized. This makes them easy to identify in a text.

Where possible, using proper nouns in place of common nouns helps bring precision to a student’s writing.

Some examples of proper nouns include:

People: Mrs Casey, J.K. Rowling, Nikola Tesla, Pablo Picasso, Billie Eilish.

Places: Australia, San Francisco, Llandovery, The White House, Gardens of Versailles.

Things: Bulgarian, The World Cup, Rolling Stone, The Lion King, The Hunger Games.

Nouns Teaching Activity: Common vs Proper Nouns

  • Provide students with books suitable for their current reading level.
  • Instruct students to go through a page or two and identify all the nouns.
  • Ask students to sort these nouns into two lists according to whether they are common nouns or proper nouns.

As mentioned, all common and proper nouns can be further classified as either concrete or abstract .

A concrete noun is any noun that can be experienced through one of the five senses. In other words, if you can see, smell, hear, taste, or touch it, then it’s a concrete noun.

Some examples of concrete nouns include:

Abstract nouns refer to those things that can’t be experienced or identified through the five senses.

They are not physical things we can perceive but intangible concepts and ideas, qualities and states.

Some examples of abstract nouns include:

Nouns Teaching Activity: Concrete Vs. Abstract Nouns

  • Provide students with a book suitable for their current reading level.
  • Instruct students to go through a page or two and identify all the nouns (the lists from Practice Activity #1 may be suitable).
  • This time, ask students to sort these nouns into two lists according to whether they are concrete or abstract nouns.

A collective noun is the name of a group of people or things. That is, a collective noun always refers to more than one of something.

Some examples of collective nouns include:

People: a board of directors, a team of football players, a cast of actors, a band of musicians, a class of students.

Places: a range of mountains, a suite of rooms, a union of states, a chain of islands.

Things: a bale of hay, a constellation of stars, a bag of sweets, a school of fish, a flock of seagulls.

Countable nouns are nouns that refer to things that can be counted. They come in two flavors: singular and plural .

In their singular form, countable nouns are often preceded by the article, e.g. a , an , or the .

In their plural form, countable nouns are often preceded by a number. They can also be used in conjunction with quantifiers such as a few and many .

Some examples of countable nouns include:


Also known as mass nouns, uncountable nouns are, as their name suggests, impossible to count. Abstract ideas such as bravery and compassion are uncountable, as are things like liquid and bread .

These types of nouns are always treated in the singular and usually do not have a plural form. 

They can stand alone or be used in conjunction with words and phrases such as any , some , a little , a lot of , and much .

Some examples of uncountable nouns include:


Nouns teaching activity: how many can you list .

  • Organize students into small groups to work collaboratively.
  • Challenge students to list as many countable and uncountable nouns as they can in ten minutes.
  • To make things more challenging, stipulate that there must be an uncountable noun and a countable noun to gain a point.
  • The winning group is the one that scores the most points.

Parts of Speech | parts of speech square 1 | Parts of Speech: The Ultimate Guide for Students and Teachers |

Without a verb, there is no sentence! Verbs are the words we use to represent both internal and external actions or states of being. Without a verb, nothing happens.

Parts of Speech - What is a verb?

There are many different types of verbs. Here, we will look at five important verb forms organised according to the jobs they perform:

Dynamic Verbs

Stative verbs, transitive verbs, intransitive verbs, auxiliary verbs.

Each verb can be classified as being either an action or a stative verb.

Dynamic or action verbs describe the physical activity performed by the subject of a sentence. This type of verb is usually the first we learn as children. 

For example, run , hit , throw , hide , eat , sleep , watch , write , etc. are all dynamic verbs, as is any action performed by the body.

Let’s see a few examples in sentences:

  • I jogged around the track three times.
  • She will dance as if her life depends on it.
  • She took a candy from the bag, unwrapped it, and popped it into her mouth.

If a verb doesn’t describe a physical activity, then it is a stative verb.

Stative verbs refer to states of being, conditions, or mental processes. Generally, we can classify stative verbs into four types:

  • Emotions/Thoughts

Some examples of stative verbs include: 

Senses: hurt, see, smell, taste, hear, etc.

Emotions: love, doubt, desire, remember, believe, etc.

Being: be, have, require, involve, contain, etc.

Possession: want, include, own, have, belong, etc.

Here are some stative verbs at work in sentences:

  • That is one thing we can agree on.
  • I remember my first day at school like it was yesterday.
  • The university requires students to score at least 80%.
  • She has only three remaining.

Sometimes verbs can fit into more than one category, e.g., be , have , look , see , e.g.,

  • She looks beautiful. (Stative)
  • I look through the telescope. (Dynamic)

Each action or stative verb can also be further classified as transitive or intransitive .

A transitive verb takes a direct object after it. The object is the noun, noun phrase, or pronoun that has something done to it by the subject of the sentence.

We see this in the most straightforward English sentences, i.e., the Subject-Verb-Object or SVO sentence. 

Here are two examples to illustrate. Note: the subject of each sentence is underlined, and the transitive verbs are in bold.

  • The teacher answered the student’s questions.
  • She studies languages at university.
  • My friend loves cabbage.

Most sentences in English employ transitive verbs.

An intransitive verb does not take a direct object after it. It is important to note that only nouns, noun phrases, and pronouns can be classed as direct objects. 

Here are some examples of intransitive verbs – notice how none of these sentences has direct objects after their verbs.

  • Jane’s health improved .
  • The car ran smoothly.
  • The school opens at 9 o’clock.

Auxiliary verbs, also known as ‘helping’ verbs, work with other verbs to affect the meaning of a sentence. They do this by combining with a main verb to alter the sentence’s tense, mood, or voice.

Auxiliary verbs will frequently use not in the negative.

There are relatively few auxiliary verbs in English. Here is a list of the main ones:

  • be (am, are, is, was, were, being)
  • do (did, does, doing)
  • have (had, has, having)

Here are some examples of auxiliary verbs (in bold) in action alongside a main verb (underlined).

She is working as hard as she can.

  • You must not eat dinner until after five o’clock.
  • The parents may come to the graduation ceremony.

The Subject-Auxiliary Inversion Test

To test whether or not a verb is an auxiliary verb, you can use the Subject-Auxiliary Inversion Test .

  • Take the sentence, e.g:
  • Now, invert the subject and the suspected auxiliary verb to see if it creates a question.

Is she working as hard as she can?

  • Can it take ‘not’ in the negative form?

She is not working as hard as she can.

  • If the answer to both of these questions is yes, you have an auxiliary verb. If not, you have a full verb.

Verbs Teaching Activity: Identify the Verbs

  • Instruct students to go through an appropriate text length (e.g., paragraph, page, etc.) and compile a list of verbs.
  • In groups, students should then discuss and categorize each verb according to whether they think they are dynamic or stative, transitive or intransitive, and/or auxiliary verbs.

The job of an adjective is to modify a noun or a pronoun. It does this by describing, quantifying, or identifying the noun or pronoun. Adjectives help to make writing more interesting and specific. Usually, the adjective is placed before the word it modifies.

what part of speech is feature article

As with other parts of speech, not all adjectives are the same. There are many different types of adjectives and, in this article, we will look at:

Descriptive Adjectives

  • Degrees of Adjectives

Quantitative Adjectives

Demonstrative adjectives, possessive adjectives, interrogative adjectives, proper adjectives.

Descriptive adjectives are what most students think of first when asked what an adjective is. Descriptive adjectives tell us something about the quality of the noun or pronoun in question. For this reason, they are sometimes referred to as qualitative adjectives .

Some examples of this type of adjective include:

  • hard-working

In sentences, they look like this:

  • The pumpkin was enormous .
  • It was an impressive feat of athleticism I ever saw.
  • Undoubtedly, this was an exquisite vase.
  • She faced some tough competition.

Degrees of Adjectives 

Descriptive adjectives have three degrees to express varying degrees of intensity and to compare one thing to another. These degrees are referred to as positive , comparative , and superlative .

The positive degree is the regular form of the descriptive adjective when no comparison is being made, e.g., strong .

The comparative degree is used to compare two people, places, or things, e.g., stronger .

There are several ways to form the comparative, methods include:

  • Adding more or less before the adjective
  • Adding -er to the end of one syllable adjectives
  • For two-syllable adjectives ending in y , change the y to an i and add -er to the end.

The superlative degree is typically used when comparing three or more things to denote the upper or lowermost limit of a quality, e.g., strongest .

There are several ways to form the superlative, including:

  • Adding most or least before the adjective
  • Adding -est to the end of one syllable adjectives
  • For two-syllable adjectives ending in y , change the y to an i and add -est to the end.

There are also some irregular adjectives of degree that follow no discernible pattern that must be learned off by students, e.g., good – better – best .

Let’s take a look at these degrees of adjectives in their different forms.

Let’s take a quick look at some sample sentences:

  • It was a beautiful example of kindness. 


  • The red is nice, but the green is prettier .


  • This mango is the most delicious fruit I have ever tastiest. 

Quantitive adjectives provide information about how many or how much of the noun or pronoun.

Some quantitive adjectives include:

  • She only ate half of her sandwich.
  • This is my first time here.
  • I would like three slices, please.
  • There isn’t a single good reason to go.
  • There aren’t many places like it.
  • It’s too much of a good thing.
  • I gave her a whole box of them.

A demonstrative adjective identifies or emphasizes a noun’s place in time or space. The most common demonstrative adjectives are this , that , these , and those .

Here are some examples of demonstrative adjectives in use:

  • This boat is mine.
  • That car belongs to her.
  • These shoes clash with my dress.
  • Those people are from Canada.

Possessive adjectives show ownership, and they are sometimes confused with possessive pronouns.

The most common possessive adjectives are my , your , his , her , our , and their .

Students need to be careful not to confuse these with possessive pronouns such as mine , yours , his (same in both contexts), hers , ours , and theirs .

Here are some examples of possessive adjectives in sentences:

  • My favorite food is sushi.
  • I would like to read your book when you have finished it.
  • I believe her car is the red one.
  • This is their way of doing things.
  • Our work here is done.

Interrogative adjectives ask questions, and, in common with many types of adjectives, they are always followed by a noun. Basically, these are the question words we use to start questions. Be careful however, interrogative adjectives modify nouns. If the word after the question word is a verb, then you have an interrogative adverb on hand.

Some examples of interrogative adjectives include what , which , and whose .

Let’s take a look at these in action:

  • What drink would you like?
  • Which car should we take?
  • Whose shoes are these?

Please note: Whose can also fit into the possessive adjective category too.

We can think of proper adjectives as the adjective form of proper nouns – remember those? They were the specific names of people, places, and things and need to be capitalized.

Let’s take the proper noun for the place America . If we wanted to make an adjective out of this proper noun to describe something, say, a car we would get ‘ American car’.

Let’s take a look at another few examples:

  • Joe enjoyed his cup of Ethiopian coffee.
  • My favorite plays are Shakespearean tragedies.
  • No doubt about it, Fender guitars are some of the best in the world.
  • The Mona Lisa is a fine example of Renaissance art.

Though it may come as a surprise to some, articles are also adjectives as, like all adjectives, they modify nouns. Articles help us determine a noun’s specification. 

For example, ‘a’ and ‘an’ are used in front of an unspecific noun, while ‘the’ is used when referring to a specific noun.

Let’s see some articles as adjectives in action!

  • You will find an apple inside the cupboard.
  • This is a car.
  • The recipe is a family secret.

Adjectives Teaching Activity: Types of Adjective Tally

  • Choose a suitable book and assign an appropriate number of pages or length of a chapter for students to work with.
  • Students work their way through each page, tallying up the number of each type of adjective they can identify using a table like the one below:
  • Note how degrees of adjective has been split into comparative and superlative. The positive forms will take care of in the descriptive category.
  • You may wish to adapt this table to exclude the easier categories to identify, such as articles and demonstrative, for example.

Parts of Speech - What is an adverb?

Traditionally, adverbs are defined as those words that modify verbs, but they do so much more than that. They can be used not only to describe how verbs are performed but also to modify adjectives, other adverbs, clauses, prepositions, or entire sentences.

With such a broad range of tasks at the feet of the humble adverb, it would be impossible to cover every possibility in this article alone. However, there are five main types of adverbs our students should familiarize themselves with. These are:

Adverbs of Manner

Adverbs of time, adverbs of frequency, adverbs of place, adverbs of degree.

Adverbs of manner describe how or the way in which something happens or is done. This type of adverb is often the first type taught to students. Many of these end with -ly . Some common examples include happily , quickly , sadly , slowly , and fast .

Here are a few taster sentences employing adverbs of manner:

  • She cooks Chinese food well .
  • The children played happily together.
  • The students worked diligently on their projects.
  • Her mother taught her to cross the road carefully .
  • The date went badly .

Adverbs of time indicate when something happens. Common adverbs of time include before , now , then , after , already , immediately , and soon .

Here are some sentences employing adverbs of time:

  • I go to school early on Wednesdays.
  • She would like to finish her studies eventually .
  • Recently , Sarah moved to Bulgaria.
  • I have already finished my homework.
  • They have been missing training lately .

While adverbs of time deal with when something happens, adverbs of frequency are concerned with how often something happens. Common adverbs of frequency include always , frequently , sometimes , seldom , and never .

Here’s what they look like in sentences:

  • Harry usually goes to bed around ten.
  • Rachel rarely eats breakfast in the morning.
  • Often , I’ll go home straight after school.
  • I occasionally have ketchup on my pizza.
  • She seldom goes out with her friends.

Adverbs of place, as the name suggests, describe where something happens or where it is. They can refer to position, distance, or direction. Some common adverbs of place include above , below , beside , inside , and anywhere .

Check out some examples in the sentences below:

  • Underneath the bridge, there lived a troll.
  • There were pizzerias everywhere in the city.
  • We walked around the park in the pouring rain.
  • If the door is open, then go inside .
  • When I am older, I would like to live nearby .

Adverbs of degree express the degree to which or how much of something is done. They can also be used to describe levels of intensity. Some common adverbs of degree include barely , little , lots , completely , and entirely .

Here are some adverbs of degree at work in sentences:

  • I hardly noticed her when she walked into the room.
  • The little girl had almost finished her homework.
  • The job was completely finished.
  • I was so delighted to hear the good news.
  • Jack was totally delighted to see Diane after all these years.

Adverb Teaching Activity: The Adverb Generator

  • Give students a worksheet containing a table divided into five columns. Each column bears a heading of one of the different types of adverbs ( manner , time , frequency , place , degree ).
  • Challenge each group to generate as many different examples of each adverb type and record these in the table.
  • The winning group is the one with the most adverbs. As a bonus, or tiebreaker, task the students to make sentences with some of the adverbs.

Parts of speech - what is a pronoun?

Pronouns are used in place of a specific noun used earlier in a sentence. They are helpful when the writer wants to avoid repetitive use of a particular noun such as a name. For example, in the following sentences, the pronoun she is used to stand for the girl’s name Mary after it is used in the first sentence. 

Mary loved traveling. She had been to France, Thailand, and Taiwan already, but her favorite place in the world was Australia. She had never seen an animal quite as curious-looking as the duck-billed platypus.

We also see her used in place of Mary’s in the above passage. There are many different pronouns and, in this article, we’ll take a look at:

Subject Pronouns

Object pronouns, possessive pronouns, reflexive pronouns, intensive pronouns, demonstrative pronouns, interrogative pronouns.

Subject pronouns are the type of pronoun most of us think of when we hear the term pronoun . They operate as the subject of a verb in a sentence. They are also known as personal pronouns.

The subject pronouns are:

Here are a few examples of subject pronouns doing what they do best:

  • Sarah and I went to the movies last Thursday night.
  • That is my pet dog. It is an Irish Wolfhound.
  • My friends are coming over tonight, they will be here at seven.
  • We won’t all fit into the same car.
  • You have done a fantastic job with your grammar homework!

Object pronouns operate as the object of a verb, or a preposition, in a sentence. They act in the same way as object nouns but are used when it is clear what the object is.

The object pronouns are:

Here are a few examples of object pronouns in sentences:

  • I told you , this is a great opportunity for you .
  • Give her some more time, please.
  • I told her I did not want to do it .
  • That is for us .
  • Catherine is the girl whom I mentioned in my letter.

Possessive pronouns indicate ownership of a noun. For example, in the sentence:

These books are mine .

The word mine stands for my books . It’s important to note that while possessive pronouns look similar to possessive adjectives, their function in a sentence is different.

The possessive pronouns are:

Let’s take a look at how these are used in sentences:

  • Yours is the yellow jacket.
  • I hope this ticket is mine .
  • The train that leaves at midnight is theirs .
  • Ours is the first house on the right.
  • She is the person whose opinion I value most.
  • I believe that is his .

Reflexive pronouns are used in instances where the object and the subject are the same. For example, in the sentence, she did it herself , the words she and herself refer to the same person.

The reflexive pronoun forms are:

Here are a few more examples of reflexive pronouns at work:

  • I told myself that numerous times.
  • He got himself a new computer with his wages.
  • We will go there ourselves .
  • You must do it yourself .
  • The only thing to fear is fear itself .

This type of pronoun can be used to indicate emphasis. For example, when we write, I spoke to the manager herself , the point is made that we talked to the person in charge and not someone lower down the hierarchy. 

Similar to the reflexive pronouns above, we can easily differentiate between reflexive and intensive pronouns by asking if the pronoun is essential to the sentence’s meaning. If it isn’t, then it is used solely for emphasis, and therefore, it’s an intensive rather than a reflexive pronoun.

Often confused with demonstrative adjectives, demonstrative pronouns can stand alone in a sentence.

When this , that , these , and those are used as demonstrative adjectives they come before the noun they modify. When these same words are used as demonstrative pronouns, they replace a noun rather than modify it.

Here are some examples of demonstrative pronouns in sentences:

  • This is delicious.
  • That is the most beautiful thing I have ever seen.
  • These are not mine.
  • Those belong to the driver.

Interrogative pronouns are used to form questions. They are the typical question words that come at the start of questions, with a question mark coming at the end. The interrogative pronouns are:

Putting them into sentences looks like this:

  • What is the name of your best friend?
  • Which of these is your favourite?
  • Who goes to the market with you?
  • Whom do you think will win?
  • Whose is that?

Pronoun Teaching Activity: Pronoun Review Table

  • Provide students with a review table like the one below to revise the various pronoun forms.
  • They can use this table to help them produce independent sentences.
  • Once students have had a chance to familiarize themselves thoroughly with each of the different types of pronouns, provide the students with the headings and ask them to complete a table from memory.  


Parts of speech - What is a preposition?

Prepositions provide extra information showing the relationship between a noun or pronoun and another part of a sentence. These are usually short words that come directly before nouns or pronouns, e.g., in , at , on , etc.

There are, of course, many different types of prepositions, each relating to particular types of information. In this article, we will look at:

Prepositions of Time

Prepositions of place, prepositions of movement, prepositions of manner, prepositions of measure.

  • Preposition of Agency
  • Preposition of Possession
  • Preposition of Source

Phrasal Prepositions

It’s worth noting that several prepositional words make an appearance in several different categories of prepositions.

Prepositions of time indicate when something happens. Common prepositions of time include after , at , before , during , in , on .

Let’s see some of these at work:

  • I have been here since Thursday.
  • My daughter was born on the first of September.
  • He went overseas during the war.
  • Before you go, can you pay the bill, please?
  • We will go out after work.

Sometimes students have difficulty knowing when to use in , on , or at . These little words are often confused. The table below provides helpful guidance to help students use the right preposition in the right context.

The prepositions of place, in , at , on , will be instantly recognisable as they also double as prepositions of time. Again, students can sometimes struggle a little to select the correct one for the situation they are describing. Some guidelines can be helpful.

  • If something is contained or confined inside, we use in .
  • If something is placed upon a surface, we use on .
  • If something is located at a specific point, we use at .

A few example sentences will assist in illustrating these:

  • He is in the house.
  • I saw it in a magazine.
  • In France, we saw many great works of art.
  • Put it on the table.
  • We sailed on the river.
  • Hang that picture on the wall, please.
  • We arrived at the airport just after 1 pm.
  • I saw her at university.
  • The boy stood at the window.

Usually used with verbs of motion, prepositions of movement indicate movement from one place to another. The most commonly used preposition of movement is to .

Some other prepositions of movement include:

Here’s how they look in some sample sentences:

  • The ball rolled across the table towards me.
  • We looked up into the sky.
  • The children ran past the shop on their way home.
  • Jackie ran down the road to greet her friend.
  • She walked confidently through the curtains and out onto the stage.

Preposition of manner shows us how something is done or how it happens. The most common of these are by , in , like , on , with .

Let’s take a look at how they work in sentences:

  • We went to school by bus.
  • During the holidays, they traveled across the Rockies on foot.
  • Janet went to the airport in a taxi.
  • She played soccer like a professional.
  • I greeted her with a smile.

Prepositions of measure are used to indicate quantities and specific units of measurement. The two most common of these are by and of .

Check out these sample sentences:

  • I’m afraid we only sell that fabric by the meter.
  • I will pay you by the hour.
  • She only ate half of the ice cream. I ate the other half.
  • A kilogram of apples is the same weight as a kilogram of feathers.

Prepositions of Agency

These prepositions indicate the causal relationship between a noun or pronoun and an action. They show the cause of something happening. The most commonly used prepositions of agency are by and with .

Here are some examples of their use in sentences:

  • The Harry Potter series was written by J.K. Rowling.
  • This bowl was made by a skilled craftsman.
  • His heart was filled with love.
  • The glass was filled with water.

Prepositions of Possession

Prepositions of possessions indicate who or what something belongs to. The most common of these are of , to , and with .

Let’s take a look:

  • He is the husband of my cousin.
  • He is a friend of the mayor.
  • This once belonged to my grandmother.
  • All these lands belong to the Ministry.
  • The man with the hat is waiting outside.
  • The boy with the big feet tripped and fell.

Prepositions of Source

Prepositions of source indicate where something comes from or its origins. The two most common prepositions of source are from and by . There is some crossover here with prepositions of agency.

Here are some examples:

  • He comes from New Zealand.
  • These oranges are from our own orchard.
  • I was warmed by the heat of the fire.
  • She was hugged by her husband.
  • The yoghurt is of Bulgarian origin.

Phrasal prepositions are also known as compound prepositions. These are phrases of two or more words that function in the same way as prepositions. That is, they join nouns or pronouns to the rest of the sentence.

Some common phrasal prepositions are:

  • According to
  • For a change
  • In addition to
  • In spite of
  • Rather than
  • With the exception of

Students should be careful of overusing phrasal prepositions as some of them can seem clichéd. Frequently, it’s best to say things in as few words as is necessary.

Preposition Teaching Activity: Pr eposition Sort

  • Print out a selection of the different types of prepositions on pieces of paper.
  • Organize students into smaller working groups and provide each group with a set of prepositions.
  • Using the headings above as categories, challenge students to sort the prepositions into the correct groups. Note that some prepositions will comfortably fit into more than one group.
  • The winning group is the one to sort all prepositions correctly first.
  • As an extension exercise, students can select a preposition from each category and write a sample sentence for it.


Parts of Speech - What is a conjunction?

Conjunctions are used to connect words, phrases, and clauses. There are three main types of conjunction that are used to join different parts of sentences. These are:

  • Coordinating
  • Subordinating
  • Correlative

Coordinating Conjunctions

These conjunctions are used to join sentence components that are equal such as two words, two phrases, or two clauses. In English, there are seven of these that can be memorized using the mnemonic FANBOYS:

Here are a few example sentences employing coordinating conjunctions:

  • As a writer, he needed only a pen and paper.
  • I would describe him as strong but lazy.
  • Either we go now or not at all.

Subordinating Conjunctions

Subordinating conjunctions are used to introduce dependent clauses in sentences. Basically, dependent clauses are parts of sentences that cannot stand as complete sentences on their own. 

Some of the most common subordinate conjunctions are: 

Let’s take a look at some example sentences:

  • I will complete it by Tuesday if I have time.
  • Although she likes it, she won’t buy it.
  • Jack will give it to you after he finds it.

Correlative Conjunctions

Correlative conjunctions are like shoes; they come in pairs. They work together to make sentences work. Some come correlative conjunctions are:

  • either / or
  • neither / nor
  • Not only / but also

Let’s see how some of these work together:

  • If I were you, I would get either the green one or the yellow one.
  • John wants neither pity nor help.
  • I don’t know whether you prefer horror or romantic movies.

Conjunction Teaching Activity: Conjunction Challenge

  • Organize students into Talking Pairs .
  • Partner A gives Partner B an example of a conjunction.
  • Partner B must state which type of conjunction it is, e.g. coordinating, subordinating, or correlative.
  • Partner B must then compose a sentence that uses the conjunction correctly and tell it to Partner A.
  • Partners then swap roles.


parts of speech - What is an interjection?

Interjections focus on feelings and are generally grammatically unrelated to the rest of the sentence or sentences around them. They convey thoughts and feelings and are common in our speech. They are often followed by exclamation marks in writing. Interjections include expressions such as:

  • Eww! That is so gross!
  • Oh , I don’t know. I’ve never used one before.
  • That’s very… err …generous of you, I suppose.
  • Wow! That is fantastic news!
  • Uh-Oh! I don’t have any more left.

Interjection Teaching Activity: Create a scenario

  • Once students clearly understand what interjections are, brainstorm as a class as many as possible.
  • Write a master list of interjections on the whiteboard.
  • Partner A suggests an interjection word or phrase to Partner B.
  • Partner B must create a fictional scenario where this interjection would be used appropriately.

With a good grasp of the fundamentals of parts of speech, your students will now be equipped to do a deeper dive into the wild waters of English grammar. 

To learn more about the twists and turns of English grammar, check out our comprehensive article on English grammar here.


Parts of Speech | FREE DOWNLOAD | Parts of Speech: The Ultimate Guide for Students and Teachers |


Parts of Speech | 5 | Parts of Speech: The Ultimate Guide for Students and Teachers |


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Speech vs Feature Article

This speech first appeared in the Irish Independent Written Word Supplement on Monday 26th January 2015

To help you to appreciate the stylistic difference between the spoken and the written word, I’ve used the same topic, the same structure and the same ideas but I’ve transformed it into a speech. Your task is to spot the differences between them.

There are 7 significant differences as well as lots of subtle rephrasing so that it sounds like spoken language – something to be listened to rather than read off a page.

Speech on Mindfulness

Hi everyone. You’re very welcome. If this is your first time attending a meeting of the Literary and Debating society, please give us your email details on the sheet we’re passing around so we can contact you about future events. In the meantime, it’s my great pleasure to welcome Susan Mullane to the podium. Susan will be speaking to us today about mindfulness.

Hi everyone, I’m Susan as you’ve just heard. I’m studying to be a journalist and I recently wrote a feature on mindfulness that I’m currently trying to get published.

Before we debate the merits of mindfulness I’d like you to experience them, so you can pass judgement actually knowing what it is we’re talking about. So I’d like everyone in the room now to close your eyes. I need everyone to do this so no-one feels silly. Now breathe in. Feel the oxygen flood your core. Feel it flow into your limbs. Cleanse your mind of all thoughts. Now focus your attention, slowly, on each part of your body in turn. Become aware of your feet. Now move your awareness up to your calves… into your thighs… now your abdomen… your hands… arms… chest… shoulders… neck… head… face. Listen to the sounds in the room. The tick of the clock. The breathing of the person beside you. The birds outside the window. Allow your thoughts to wander and as each new thought appears, let it flow away. Focus on the now. Be aware of your body. Become aware of your breadth. Slowly breathe in through your nose and out through your mouth. And repeat. In through the nose and out through the mouth. Now when you’re ready, slowly open your eyes.

Ok, how do you feel? ( giggles in response )

Can you share with the room? ( pointing to someone in the front row ). Silly, ok! Anyone else? ( taking a show of hands ) Yep, in the third row? Relaxed… sleepy. Anyone up the back? Cynical. Ok, so you don’t think this works? Who else is a cynic? ( show of hands )

Well, let me assure you you’re not alone. Lots of people, when they hear the term mindfulness, the first thing that springs to mind is hippies in a Volkswagen camper van. It’s all a bit touchy-feely isn’t it? The second major criticism of mindfulness people have is that as it’s entered the mainstream, it’s lost any real connection to its roots in Buddhism. So critics say it’s no longer about the quest to discover what it means to live a moral life and that corporations have just put on a show of caring about how their employees feel but really they just want to get more work out of you. As in, reducing stress via ten minutes of mindfulness a day boosts your employees productivity and that’s a hell of a lot cheaper than hiring extra staff! In fact, critics of the way mindfulness courses have been churned out by consultants to big business have given it a new name and labelled it McMindfulness ( click to show image of a cow meditating outside a well-known fast food chain on screen. Pause for laughter )

But the question I want to ask today is does that matter? There is no doubt that mindfulness is a full on craze. It’s been brought into schools and nursing homes and even prisons. But just because something is popular, does not make it worthless. And just because it’s been adapted from its original form does not make it toxic.

So I decided I’d focus instead on answering one basic question: DOES IT WORK?

I’ve going to play you a little video now. It’s full of vox pops from people who reckon it does work:

Video: Direct to camera: ‘ Hi I’m Karen Miles. I’m the founder and I’m a big believer in mindfulness. Here are some of the things Irish people who use my site have said about practising mindfulness in their daily lives’ .

Quotes accompanied by soothing classical music “ I’ve seen it transform my own life ” Clare, 52, Mayo. “ I am so glad I did this. I find I get a lot less stressed about the small stuff ” Annette, 35, Louth. “ Feeling calm. Have been following the tips on your site for four months and I don’t know myself” Jennifer, via facebook. “ @seanlala Thanks @meinmind Your site helped me to get through the stress of my exams ” Sean, 17.

I’m going to stop the video there because it goes on for three more minutes but it’s really just more of the same. Now there is an argument to say that the placebo effect could play a role here. If you try something and you believe it’s going to make you better, then chances are it will have a positive impact on your health. So the next thing I did was look for research studies. There are a lot of studies out there on meditation, but not as many on mindfulness. But I did find a metaanalysis collecting together all of the mindfulness studies that do exist. It was created by the US Agency for Healthcare Research and Quality and they concluded only last year, in 2014, that mindfulness does indeed have an effect. They say, and I quote “ Following a mindfulness programme reduces many of the most toxic elements of stress, including anxiety and depression ”.

Of course they also include a warning about the limitations of this practice. So there’s no evidence that it alters your eating habits, helps you lose weight or to sleep better. And it’s not better than exercise or behavioural therapies. To which I reply, who cares? Singing daily doesn’t make me better at playing the piano but that does not negate the joy of singing in my life. If I can find something that I can work into my daily practice and build into my life, that makes me less anxious, less stressed and less likely to become depressed, then hallelujah, bring it on.

So having debated the pros and cons my conclusion is this. If you can afford behavioural therapies, by all means do that too. Eating healthily and getting exercise remain important, but this isn’t an either or scenario. Do it all if it makes you feel better.

My mother used to say ‘ Everything that helps, helps ‘. When I was a teenager, there was one day I was prowling the house, completely stressed out about the Leaving Cert, and I guess I was annoying her because she suddenly went to the press, hauled out a stack of old plates and said ‘would you ever go and smash those. It might calm you down’. So I did. And it was wonderful. I wish I had a whole stack of plates I could give you right now so we could go outside and have a plate smashing festival. I’ll never forget the liberating joy of wilful destruction I experienced that day. I was aware of my body, I was aware of my surroundings, I was caught up in the present moment and I felt a hell of a lot better afterwards. It didn’t last forever, that feeling, but it did last for a couple of days.

So those are the conclusions I came to, which is that it won’t do you any harm and chances are it will do you a lot of good! Now I need to ask you to show a thumbs up or a thumbs down for mindfulness, and then we can all launch into a more detailed debate, but before we do that there is one more argument in favour of mindfulness that I haven’t mentioned. I think here in Ireland it’s something we need. I think we’ve been severely challenged these last couple of years by austerity, and by high unemployment and by emigration. And I think embracing mindfulness has not been about pretending everything is fine. I think its popularity is a sign that we know everything’s not fine and we’re trying to do something about it and find coping mechanisms that’ll help us to get through it.

So before we vote, will everyone again please close your eyes. Breathe in. Feel the oxygen flood your body, feel it flow into your limbs. Focus your mind on your feet… calves… thighs… abdomen… hands… arms… chest… shoulders… neck… head… face. Listen to the sounds in the room. And now you can open your eyes. Thanks for your attention.

I think you’re ready to vote!

*For the list of the most significant differences between the article and the speech, click here.

One response to “ Speech vs Feature Article ”

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what part of speech is feature article

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Grammar: Main Parts of Speech

Definitions and examples.

The name of something, like a person, animal, place, thing, or concept. Nouns are typically used as subjects, objects, objects of prepositions, and modifiers of other nouns.

  • I = subject
  • the dissertation = object
  • in Chapter 4 = object of a preposition
  • research = modifier

This expresses what the person, animal, place, thing, or concept does. In English, verbs follow the noun.

  • It takes a good deal of dedication to complete a doctoral degree.
  • She studied hard for the test.
  • Writing a dissertation is difficult. (The "be" verb is also sometimes referred to as a copula or a linking verb. It links the subject, in this case "writing a dissertation," to the complement or the predicate of the sentence, in this case, "hard.")

This describes a noun or pronoun. Adjectives typically come before a noun or after a stative verb, like the verb "to be."

  • Diligent describes the student and appears before the noun student .
  • Difficult is placed after the to be verb and describes what it is like to balance time.

Remember that adjectives in English have no plural form. The same form of the adjective is used for both singular and plural nouns.

  • A different idea
  • Some different ideas
  • INCORRECT: some differents ideas

This gives more information about the verb and about how the action was done. Adverbs tells how, where, when, why, etc. Depending on the context, the adverb can come before or after the verb or at the beginning or end of a sentence.

  • Enthusiastically describes how he completed the course and answers the how question.
  • Recently modifies the verb enroll and answers the when question.
  • Then describes and modifies the entire sentence. See this link on transitions for more examples of conjunctive adverbs (adverbs that join one idea to another to improve the cohesion of the writing).

This word substitutes for a noun or a noun phrase (e.g. it, she, he, they, that, those,…).

  • they = applicants
  • He = Smith; that = ideas; those = those ideas

This word makes the reference of the noun more specific (e.g. his, her, my, their, the, a, an, this, these, … ).

  • Jones published her book in 2015.
  • The book was very popular.


This comes before a noun or a noun phrase and links it to other parts of the sentence. These are usually single words (e.g., on, at, by ,… ) but can be up to four words (e.g., as far as, in addition to, as a result of, …).

  • I chose to interview teachers in the district closest to me.
  • The recorder was placed next to the interviewee.
  • I stopped the recording in the middle of the interview due to a low battery.


A word that joins two clauses. These can be coordinating (an easy way to remember this is memorizing FANBOYS = for, and, nor, but, or, yet, so) or subordinating (e.g., because, although, when, …).

  • The results were not significant, so the alternative hypothesis was accepted.
  • Although the results seem promising, more research must be conducted in this area.

Auxiliary Verbs

Helping verbs. They are used to build up complete verbs.

  • Primary auxiliary verbs (be, have, do) show the progressive, passive, perfect, and negative verb tenses .
  • Modal auxiliary verbs (can, could, may, might, must, shall, should, will, would) show a variety of meanings. They represent ability, permission, necessity, and degree of certainty. These are always followed by the simple form of the verb.
  • Semimodal auxiliary verbs (e.g., be going to, ought to, have to, had better, used to, be able to,…). These are always followed by the simple form of the verb.
  • primary: have investigated = present perfect tense; has not been determined = passive, perfect, negative form
  • The modal could shows ability, and the verb conduct stays in its simple form; the modal may shows degree of certainty, and the verb lead stays in its simple form.
  • These semimodals are followed by the simple form of the verb.

Common Endings

Nouns, verbs, adjectives, and adverbs often have unique word endings, called suffixes . Looking at the suffix can help to distinguish the word from other parts of speech and help identify the function of the word in the sentence. It is important to use the correct word form in written sentences so that readers can clearly follow the intended meaning.

Here are some common endings for the basic parts of speech. If ever in doubt, consult the dictionary for the correct word form.

Common Noun Endings

Common verb endings, common adjective endings, common adverb endings, placement and position of adjectives and adverbs, order of adjectives.

If more than one adjective is used in a sentence, they tend to occur in a certain order. In English, two or three adjectives modifying a noun tend to be the limit. However, when writing in APA, not many adjectives should be used (since APA is objective, scientific writing). If adjectives are used, the framework below can be used as guidance in adjective placement.

  • Determiner (e.g., this, that, these, those, my, mine, your, yours, him, his, hers they, their, some, our, several,…) or article (a, an, the)
  • Opinion, quality, or observation adjective (e.g., lovely, useful, cute, difficult, comfortable)
  • Physical description
  • (a) size (big, little, tall, short)
  • (b) shape (circular,  irregular, triangular)
  • (c) age (old, new, young, adolescent)
  • (d) color (red, green, yellow)
  • Origin (e.g., English, Mexican, Japanese)
  • Material (e.g., cotton, metal, plastic)
  • Qualifier (noun used as an adjective to modify the noun that follows; i.e., campus activities, rocking chair, business suit)
  • Head noun that the adjectives are describing (e.g., activities, chair, suit)

For example:

  • This (1) lovely (2) new (3) wooden (4) Italian (5) rocking (6) chair (7) is in my office.
  • Your (1) beautiful (2) green (3) French (4) silk (5) business (6) suit (7) has a hole in it.

Commas With Multiple Adjectives

A comma is used between two adjectives only if the adjectives belong to the same category (for example, if there are two adjectives describing color or two adjectives describing material). To test this, ask these two questions:

  • Does the sentence make sense if the adjectives are written in reverse order?
  • Does the sentence make sense if the word “and” is written between them?

If the answer is yes to the above questions, the adjectives are separated with a comma. Also keep in mind a comma is never used before the noun that it modifies.

  • This useful big round old green English leather rocking chair is comfortable . (Note that there are no commas here because there is only one adjective from each category.)
  • A lovely large yellow, red, and green oil painting was hung on the wall. (Note the commas between yellow, red, and green since these are all in the same category of color.)

Position of Adverbs

Adverbs can appear in different positions in a sentence.

  • At the beginning of a sentence: Generally , teachers work more than 40 hours a week.
  • After the subject, before the verb: Teachers generally work more than 40 hours a week.
  • At the end of a sentence: Teachers work more than 40 hours a week, generally .
  • However, an adverb is not placed between a verb and a direct object. INCORRECT: Teachers work generally more than 40 hours a week.

More Detailed Rules for the Position of Adverbs

  • Adverbs that modify the whole sentence can move to different positions, such as certainly, recently, fortunately, actually, and obviously.
  • Recently , I started a new job.
  • I recently started a new job.
  • I started a new job recently .
  • Many adverbs of frequency modify the entire sentence and not just the verb, such as frequently, usually, always, sometimes, often , and seldom . These adverbs appear in the middle of the sentence, after the subject.
  • INCORRECT: Frequently she gets time to herself.
  • INCORRECT: She gets time to herself frequently .
  • She has frequently exercised during her lunch hour. (The adverb appears after the first auxiliary verb.)
  • She is frequently hanging out with old friends. (The adverb appears after the to be verb.)
  • Adverbial phrases work best at the end of a sentence.
  • He greeted us in a very friendly way .
  • I collected data for 2 months .

Main Parts of Speech Video Playlist

Note that these videos were created while APA 6 was the style guide edition in use. There may be some examples of writing that have not been updated to APA 7 guidelines.

  • Mastering the Mechanics: Nouns (video transcript)
  • Mastering the Mechanics: Introduction to Verbs (video transcript)
  • Mastering the Mechanics: Articles (video transcript)
  • Mastering the Mechanics: Introduction to Pronouns (video transcript)
  • Mastering the Mechanics: Modifiers (video transcript)

Writing Tools: Dictionary and Thesaurus Refresher Video

Note that this video was created while APA 6 was the style guide edition in use. There may be some examples of writing that have not been updated to APA 7 guidelines.

  • Writing Tools: Dictionary and Thesaurus Refresher (video transcript)

Related Resources


Knowledge Check: Main Parts of Speech

Didn't find what you need? Email us at [email protected] .

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How to Analyse a Feature Article? (IBDP Paper 1)

Paper 1 in English appears challenging and nerve-wracking for students. It is without a doubt one of the most difficult papers for IBDP students. You have no idea what the questions are being asked. That is, after all, the challenge of this paper. The IB evaluates your ability to analyse a guided textual analysis and to write a commentary based on your findings.

What is a commentary?

It's a thorough breakdown of the visual text. The students must make inferences, analyse the picture, and interpret it. They should present their findings in the form of an essay.

What are the types of questions in Paper 1?

For IB English Literature SL and HL, Text A is always a poem and Text B is always a prose excerpt from a novel or short story.

For IB English Language and Literature SL, Text A and Text B can come from a broad variety of sources, including magazines, editorials, speeches, interview scripts, instruction manuals, cartoon strips, and more.

How to analyse a feature article?

The following objectives must be commented on while analysing a feature article. And they follow this order.

1. Context(s) Identify text is intended for online or print interest for people in that subject matter. Feature article is a result of lengthy research and editing. So all the information provided will be well investigated.

2. Purpose(s) The writer’s intent can be to inform the people about a person, place or phenomena.

3. Stylistic Device(s)

  • The heading and subheading should be catchy, this can attract the attention and curiosity of the reader.
  • In most of the cases there will be an image in the feature article. Comment on the image and the caption below the image usually it will be related to the topic or context.
  • Notice the writer’s use of language and how he’s trying to convey his point through methods such as: sympathy, tone, emotive language etc.
  • An introduction should hook or lead the reader. Lede can be used here, it is the opening sentence or paragraph, summarizing the most important aspects of the story.
  • A body tells the story through combining facts, background research, personal discoveries, interviews etc. It is the main part of the article.
  • A conclusion should leave an impression on the reader, by showing some enlargement of understanding that the writer has discovered through the investigation.
  • You should comment on the figurative language, elements of narrative, imagery, direct and indirect speech if any.

Practice makes perfect , as the saying goes, and the more you practise writing and perfecting your essay, the better it will become. After you've completed the first analysis, go through it again and again until it's perfect. Then articulate the same in a coherent and effective way. Paper 1 requires more effort, it’s natural that you practice with more past papers.

Having trouble understanding these concepts? Do you want assistance from a subject matter expert? Here, at Vidyalai we help your child achieve the grade they aspire for. Our SMEs are trained and experienced tutors who will provide you with each and every help when required. We are just a click away. Request your first lesson now. . We guarantee 100% satisfaction on your first session, if you are not satisfied,the session will be absolutely free.

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Difference between Feature and Article

What is the difference between feature and article.

Feature as a noun is an important or main item. while Article as a noun is a story, report, or opinion piece in a newspaper, magazine, journal, internet etc.

Part of speech: noun

Definition: An important or main item. A long, prominent, article or item in the media, or the department that creates them; frequently used technically to distinguish content from news. One of the physical constituents of the face (eyes, nose, etc.; see features). A beneficial capability of a piece of software. The cast or structure of anything, or of any part of a thing, as of a landscape, a picture, a treaty, or an essay; any marked peculiarity or characteristic; as, one of the features of the landscape. Something discerned from physical evidence that helps define, identify, characterize, and interpret an archeological site. Characteristic forms or shapes of a part. For example, a hole, boss, slot, cut, chamfer, or fillet.

Part of speech: verb

Definition: To ascribe the greatest importance to something within a certain context. To star, to contain.

Definition: A story, report, or opinion piece in a newspaper, magazine, journal, internet etc. A member of a group or class An object. A part of speech that indicates, specifies and limits a noun (a, an, or the in English). In some languages the article may appear as en ending (e.g. definite article in Swedish) or there may be none (e.g. Finnish, Estonian). A section of a legal document, bylaws, etc. A person. A wench. A prime article. A handsome girl.

Example sentence: Bob Dylan's 'Blowin' In the Wind' was written into the script of 'Article 15.' It was the only song I wanted in my film. It encapsulates the spirit of exploration and salvation that my hero Ayushmann Khurrana goes through. I love the song's lyrics, especially 'How many roads must a man walk down before you call him a man? '

We hope you now know whether to use Feature or Article in your sentence.

Difference between eye and center

Brominate vs bromate, ambiguous and forked - what's the difference, difference between impress and print, width and girth - what's the difference, concrete and solid - what's the difference, low-down and emotional - what's the difference, difference between minimise and minimize, popular articles, the fascination with vinyl: vinyl vs. cd vs. streaming services, navic vs. gps vs. glonass vs. galileo: differences between navigation systems, major differences between google tv and android tv, reading on an ipad vs reading on a kindle.

People often get confused between similar sounding words or synonyms. Most of the time these words have slightly different meanings, and some time entirely different meanings. We help people discover the difference between these words.

Wordsmyth Blog

Parts of Speech explained for grades 3 to 6

“Part of speech” is one of the categories into which words of a language are grouped, according to the way they function in a sentence. Nouns, pronouns, verbs, adjectives, adverbs, prepositions, conjunctions, and interjections are the major parts of speech in English. This post contains explanations of these eight parts of speech written in simple language for upper elementary students. (Wordsmyth’s explanation of “a” and “the”–the indefinite and definite articles–is written at a higher level and designed for ESL and older students. This discussion is included in a separate post: Grammar Glossary: the definite and indefinite articles .)

  • conjunction
  • interjection
  • preposition

An adjective is a word that gives more information about a noun (or about a phrase that acts as a noun).  An adjective tells us what kind of a noun is being discussed.  Communication would be very boring if we couldn’t say that something was fantastic or miserable or fattening or downright boring!

In this sentence there are six adjectives.

I got a good grade on that long , terrible final exam that I took last week and that I thought was so hard !

An adverb is a word that gives more information about a verb, adjective or adverb, or about an entire sentence.  It tells us how, to what degree, where, or when some action happens, for example, and allows us to make some comment on what we’re about to say, or tie what we’re saying now with what we said just before.  Words like “very” or “extremely” that are used to refine the meaning of an adjective (or other adverb) are also called adverbs.

In this sentence there are nine adverbs!

Unfortunately , Natasha usually spoke so quickly that it was almost impossible to understand her, but now , when she speaks slowly , I can understand her pretty well .


A conjunction is a word that links parts of sentences together.  It can link one word to another, or larger parts of sentences to other parts.  “And” is the most common conjunction, but there are many others with quite different meanings that serve the purpose of linking.

This sentence has six conjunctions.

My cousin and I went to a movie while he was in town, but we didn’t like it ( or the noisy audience), so we left before it ended.


An interjection is a word or set phrase that stands alone, outside of any sentence.  An interjection usually expresses a strong emotion—“Oh, no!”—or an unthinking reaction—“Huh?…Hmm… Ah, ha!”

A noun is a word that names a person, place, or thing.  “Things” that are called nouns may be objects like “chair” or “shoe,” but they may also be abstract ideas like “darkness,” “anger,” or “responsibility.”  In some cases, words like “eating” and “driving” are nouns also because they can be thought of as things .

In this sentence there are seven nouns.

My sister likes running in the quiet of the park before she goes to work in the mornings .

The word “she” in this sentence acts like a noun, but words like “she,” “he,” “you,” and “me” are put into a special category called “pronouns.”


A preposition is a word that comes before a noun (or a phrase that acts like a noun), and it shows the connection, or relationship, between that noun and the previous word or phrase.  In the sentence “He went to school,” the word “to” is a preposition, and it shows the connection between “school” and “He went.”  Without prepositions, there would be confusion between sentences like “We ate in the kitchen” and “We ate the kitchen”!

In this sentence there are five prepositions.

We read about her wedding in the newspaper on Saturday, and we thought she looked like a real beauty in the photograph.

A pronoun is a kind of noun, but its meaning is not specific to one person or thing. The word “I” is a pronoun, for example. When you say “I,” you are talking about yourself , but when your sister says “I,” she is talking about herself . The meaning of the word changes depending on who is using it. Words like “I,” “we,” she,” “it,” someone,” and “what” are pronouns.  A pronoun can be used to substitute for another noun or for a phrase that acts like a noun.  If we didn’t have pronouns, we would have to say our name every time we wanted to talk about ourselves!

In this sentence there are eight pronouns!

They went to the store to get something , but they didn’t tell me what they were going to buy or why they needed it .

A verb is a word that signals an action or state of being.  It is also capable of expressing the time frame—non-past or past—of a sentence.  It usually does this by changing forms; for example, from “is” to “was,” from “play” to “played,” from “go” to “went.”

In this sentence there are five verbs.

He seemed all right, but he looked a bit tired, and he talked so quietly that I wondered if something was wrong.

Nested Entity Recognition Method Based on Multidimensional Features and Fuzzy Localization

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  • Published: 04 June 2024
  • Volume 56 , article number  196 , ( 2024 )

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what part of speech is feature article

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Nested named entity recognition (NNER) aims to identify potentially overlapping named entities. Sequence labeling method and span-based method are two commonly used methods in nested named entity recognition. However, the linear structure of sequence labeling method results in relatively poor performance, and span-based method requires traversing all spans, which brings very high time complexity. All of them fail to effectively leverage the positional dependencies between internal and external entities. In order to improve these issues, this paper proposed a nested entity recognition method based on Multidimensional Features and Fuzzy Localization (MFFL). Firstly, this method adopted the shared encoding that fused three features of characters, words, and parts of speech to obtain a multidimensional feature vector representation of the text and obtained rich semantic information in the text. Secondly, we proposed to use the fuzzy localization to assist the model in pinpointing the potential locations of entities. Finally, in the entity classification, it used a window to expand the sub-sequence and enumerate possible candidate entities and predicted the classification labels of these candidate entities. In order to alleviate the problem of error propagation and effectively learn the correlation between fuzzy localization and classification labels, we adopted multi-task learning strategy. This paper conducted several experiments on two public datasets. The experimental results showed that the proposed method achieves ideal results in both nested entity recognition and non-nested entity recognition tasks, and significantly reduced the time complexity of nested entity recognition.

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Chinese Named Entity Recognition: Applications and Challenges

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1 Introduction

The purpose of named entity recognition (NER) is to identify words or phrases that contain predetermined categories such as location, person names, and organizations. It is widely used in many tasks such as relation extraction [ 1 ], machine translation [ 2 ], and question answering systems [ 3 ]. It is worth noting that some entities have a certain nested structure, so nested named entity recognition (NNER) has become one of the current research hotspots in NER. Taking word sequence “New York University” as an example, the entity “New York” with the label type “LOC” is nested in the entity “New York University” with the label type “ORG”, as shown in Fig.  1 .

figure 1

Example diagram of nested entities

There are two common methods for nested entity recognition. The first is based on sequence labeling. Since the words in nested entities would be marked by multiple labels at the same time, it is impossible to directly use the traditional maximum probability output method for NNER, and a suitable label pattern for NNER needs to be designed. To this problem, Ju et al. proposed a hierarchical sequence labeling model, which first extracted internal entities (contained in other entities), and then feed them to the next layer to extract external entities [ 4 ]. Luo et al. proposed a bipartite planar graph network model to identify nested entities by jointly learning the outermost entities and internal constraint relationships using Bidirectional Long Short-Term Memory (Bi-LSTM) and graph convolutional networks [ 6 ]. Due to the presence of nested named entities, when a model identifies internal entities first, it can potentially cause external error propagation, affecting the detection of external entities. This error propagation is attributed to the hierarchical structure of the models, where these complex hierarchical models not only require significant computational resources but also result in significantly increased training time as the model depth increases.

The other is the span-based method, which recognizes nested entities by classifying the spans in the text. Sohrab et al. proposed an exhaustive region classification model, which list all possible regions or spans in the text to predict entities in a single layer [ 7 ]. However, this method ignored explicit boundary information, resulting in the extraction of a large number of non-entities and huge computational costs. Zheng et al. proposed a boundary-aware neural model, which first used the boundary-aware module to detect boundaries, and then used the boundary-related regions to predict entity category labels [ 8 ]. Although this method can detect boundaries, its effect is limited for languages with complex structures and lack of obvious boundaries, such as Chinese. In addition, this method does not consider the dependency between internal and external entities. Liu et al. fine-tuned BERT to encode text information and obtain span representations, combining multi-task learning to divide nested entity recognition into entity identification and entity classification tasks [ 9 ]. However, this method involves enumerating all possible spans of potential entity mentions in the sentence, resulting in excessive irrelevant regions.

Duet to the limitations of the above two methods in complex Chinese nested named entity recognition, this paper proposes to integrate them to complement each other's strengths and weaknesses. And we also observed nested entities generally have positional dependencies, that is, the positions of the two nested entities are continuous, such as the positional relationship between “New York University” and “New York” in Fig.  1 . Therefore, this paper proposed a nested entity recognition method based on Multidimensional Features and Fuzzy Localization (MFFL). The method first utilizes a fuzzy positioning module to identify potential locations of entities, and then enumerates candidate entities in a windowed fashion to identify nested entities. This approach not only leverages the characteristics of nested entity recognition but also filters out excessive irrelevant regions. Specifically, multidimensional features mainly include character, word, and part of speech features. The number of Chinese characters is huge, and each character usually has its unique semantics. Therefore, Chinese character features can provide rich semantic information and capture fine-grained information in Chinese, such as the pronunciation, morphology, and semantics of individual characters. Word features can capture higher-level semantic information, such as word meanings, phrases, and collocations. Part of speech provides a deeper understanding of the grammatical function and syntactic structure of words in sentences, helping to distinguish different types of named entities, such as person names, positions, and organization. Multidimensional features comprehensively observe sentences from different perspectives, providing diverse information. Fuzzy localization refers to first identifying entities based on window enumeration of candidate entities to identify nested entities. This method not only utilizes the characteristics of nested entity recognition, but also filters out too many irrelevant regions.

In summary, the main contributions of this paper are as follows:

We proposed a nested entity recognition method based on Multidimensional Features and Fuzzy Localization, MFFL. It mainly consists of shared encoding, a fuzzy localization, and entity classification. This method firstly locks the possible position of the entity based on the sequence labeling method, then uses the window expansion sub-sequence to effectively reduce computational costs, and finally classifies based on the span-based method.

In the shared encoding, we integrated three types of information, including characters, words, and parts-of-speech, to fully leverage the textual information in the input. This integration facilitates the high-order contextual features, thereby achieving improved recognition performance.

We proposed a 'localize-then-classify' strategy, which initially identified potential entity locations, then exploits the dependencies among nested entities by employing a window-based approach to expand sub-sequences. Finally, candidate entities enumerated from these sub-sequences are subjected to classification.

We utilized the People's Daily and Resume datasets to evaluate the performance of the proposed method for Chinese nested named entity recognition and non-nested named entity recognition respectively. The experimental results demonstrate that MFFL exhibits an improvement compared to the baseline models.

The rest of this paper is organized as follows. Section  2 discusses related work on nested named entity recognition. Section  3 describes the framework of the model and its computation process in detail. Section  4 presents the results and analysis of the MFFL experiment. Finally, we conclude the paper with a summary and future work.

2 Related Work

Early research on Chinese nested entity recognition relied on manual features or rule-based processing. Nowadays, there are mainly three mainstream methods for NNER: sequence labeling-based methods, hypergraph-based methods, and span-based methods. In addition, there are some novel methods such as state transition-based methods [ 10 ], machine reading comprehension-based methods [ 11 ], and bipartite planar graph network-based methods [ 12 ]. Here, this paper mainly discusses the first three mainstream methods.

2.1 Sequence Labeling-Based Methods

The sequence labeling-based method regards named entity recognition as a task of outputting the label sequence with the maximum probability corresponding to the sentence. It solves NNER by designing appropriate labeling patterns. Yan et al. proposed a universal decoding framework that can handle flat, nested and discontinuous entities at the same time [ 12 ]. Alex et al. adopted a unique way to identify each entity type with a CRF and recognize nested entities from the inside out by stacking CRF [ 14 ]. Ju et al. proposed a model called Layered-Bi-LSTM-CRF, which identified nested named entities by dynamically stacking flat NER layers and fully utilizing the encoding information of internal named entities to identify external named entities. Specifically, when a named entity is detected, the model merges the context representations from the LSTM to form the representation of the named entity and then passes it as input to a new flat NER layer. The number of flat NER layers is variable depending on the depth of nested named entities and is adjusted according to the input sequence [ 4 ]. The models of the above three methods are relatively complex in structure, which may require significant computational resources and longer training time. Straková et al. explored two linearization schemes for encoding nested labels. The first method is to concatenate multiple labels of nested named entities into one multi-label and then use a standard LSTM-CRF model for prediction. The second method treats the nested problem as a sequence-to-sequence problem, where the input sequence is words and the output sequence is labels [ 15 ]. This method linearizes the nested problem, simplifying the model structure, which may be beneficial for training and inference efficiency. However, it sacrifices some nested structural information, which may result in suboptimal performance for complex nesting scenarios. Wang et al. proposed a hierarchical model specifically for nested named entity recognition. Words or text regions are recursively embedded into L flat NER layers and stacked from the bottom to the top in a pyramid shape. The model also designs a reverse pyramid structure to achieve bidirectional interaction between layers [ 16 ]. Due to the complexity of nested named entities, conventional sequence labeling-based methods typically employ hierarchical structures mentioned above. If the model prioritizes the recognition of internal entities during the identification process, it may trigger external error propagation, where misidentification or omission of internal entities could mislead the model's detection of external entities, resulting in a decline in overall recognition accuracy. This error propagation phenomenon stems from the hierarchical structure design of the model, especially in complex models, which not only require significant amounts of computational resources but also experience exponential growth in training time and computational costs as the model depth increases, posing significant challenges to model optimization and deployment.

2.2 Hypergraph-Based Methods

The hypergraph-based method constructs a hypergraph based on the structure of NNER and decodes it on the hypergraph to identify nested entities. This model first represents named entities and their combinations through nodes and directed hyperedges. Five types of nodes are used at position k in the sentence to compactly capture nested named entities of different types and arbitrary lengths in the text, solving the problem of nested named entity detection [ 17 ]. Later, Muis et al. further integrated mention separators and features based on this and developed a gap-based tagging model to identify nested entity structures. The model designs eight mention separators based on the combination of three different situations. Research has shown that by using mention separators and new multi-graph representations, all possible named entity combinations can be defined and their structures accurately inferred [ 18 ]. However, Muis et al.'s method leads to structural ambiguity due to the use of local predictions. To solve this problem, Wang et al. proposed a new Segmental Hypergraph (SH) method to improve the structural ambiguity problem in the inference process of Muis et al. The model uses an unambiguous compact hypergraph representation that can encode all possible combinations of nested named entities. Based on the mention hypergraph, the model redesigns nodes to facilitate more effective exploration of the space of all span combinations of discontinuous named entities and extraction of local features. In addition, the model also uses a universal internal–external message passing algorithm that can aggregate the features of child nodes onto parent nodes for efficient inference [ 19 ]. Overall, although the hypergraph-based method has certain advantages in dealing with nested entities, it often requires a lot of time and may lose fine-grained information due to the lack of encoding of dependencies between nested entities during training and inference.

2.3 Span-Based Methods

The span-based method identifies nested entities by generating all possible spans and classifying them. Sohrab et al. considered all possible regions in a sentence and classify them as entity types or non-entities. However, this exhaustive method considers too many irrelevant regions (i.e., non-entity regions) and classifies regions separately without considering contextual information [ 7 ]. Later, Zheng et al. improved the deep exhaustive method by locating entity boundaries and jointly learning boundary detection and entity classification tasks [ 8 ]. Shen et al. regarded this task as an object detection task and proposed a two-stage named entity recognition method. In the first stage, candidate spans are generated by filtering and boundary regression on seed spans. Then in the second stage, the boundaries of pre-selected spans are determined based on type tags [ 19 ]. Gu et al. proposed a nested entity recognition method that explores semantic rules within spans and designs rule-aware and fuzzy-aware modules. The rule-aware module is used to obtain a regularity representation within spans, while the fuzzy-aware module is used to enhance span boundary features and prevent the model from focusing too much on rule information. In addition, the model constructs an orthogonal space to help the model extract heterogeneous information at the span level [ 21 ]. To address the shortcomings of existing methods in dealing with semantic relationships between candidate spans and related entities or phrases, as well as the difficulties in predicting long-distance nested entities due to the length limit of exhaustive search, Wu et al. constructed a multi-scale two-stage hierarchical model [ 22 ]. Overall, the method of exhaustive search of all possible regions in a sentence will bring huge computational costs. Although the boundary detection module can detect boundaries, boundaries are usually not obvious in Chinese text. In addition, these methods do not effectively utilize the dependencies between nested entities, which is an important consideration when dealing with Chinese nested entity recognition.

Unlike previous studies, this paper proposes a nested entity recognition method based on multi-dimensional features and fuzzy positioning. The model leverages the dependency between internal and external entities to better locate potential nested named entities, and then utilizes a window-based approach to identify nested entities. In this model, to fully utilize semantic information, we combine character, word, and part-of-speech features. Experimental results demonstrate that the dependency between internal and external entities indeed helps us perceive nested entities, reduces time complexity, and the rich semantics also enhance recognition performance.

This paper proposes a nested entity recognition method based on Multidimensional Features and Fuzzy Localization (MFFL). The overall framework is shown in Fig.  2 , where window expansion size is set to W . As shown in Fig.  2 , MFFL mainly consists of three parts: shared encoding, fuzzy localization and entity classification.

figure 2

Model architecture of MFFL model

The shared encoding is composed of a fusion of character features, word features, and part-of-speech features to obtain a multidimensional textual representation, followed by a Bi-LSTM for feature extraction. The fuzzy localization primarily consists of a Softmax classifier, with the objective of pinpointing potential entity locations. The entity classification, based on the positional dependencies among nested entities, is composed of three parts: an expanding window, candidate entity representation, and a Softmax classifier, with the ultimate goal of classifying the candidate entities into categories.

3.1 Problem Definition

The input of the model is a text sequence X consisting n tokens, X  = { x 1 , x 2 … x n }, where x i (1 ≤  i  ≤  n ) represents the i -th token in X . u s,e  = { x s , x s + 1 … x e } is a candidate entity that satisfies s  ≤  e . Let \(Y\) represents the predefined list of all entity categories (for example, “LOC”, “PER”, and “ORG” are all in Y ), and y  ∈  Y represents a certain category. The ultimate goal of this model is to determine the category y to which the candidate entity u s,e in the text X belongs.

3.2 Shared Encoding

The shared encoding is composed of two parts: vector representation and feature extraction. Initially, the jieba word segmentation tool is utilized to obtain words and parts-of-speech for each input text. Subsequently, words, parts-of-speech, and characters are counted to create vocabularies for the dataset, comprising word vocabulary, parts-of-speech vocabulary, and character vocabulary. The Word2Vec method is then employed to generate word vectors, while character vectors and part-of-speech vectors are initialized randomly. These three types of feature vectors are concatenated to form a multidimensional feature representation. Finally, Bi-LSTM is applied for feature extraction.

3.2.1 Vector Representation

For vector representation, we first collect the characters, words, and part-of-speech tags from the dataset and assign them unique indices, thereby generating character dictionary, word dictionary, and part-of-speech dictionary. Before training, we map the characters, words, and part-of-speech tags of each sample to their respective indices according to the dictionaries. During training, we retrieve vectors from the matrix based on the indices to vectorize the three types of features. Finally, we concatenate the vectors of the three types of features.

If we input a text sequence \(X=\{{x}_{1},{x}_{2}...{x}_{n}\}\) , we obtain its multiple feature representations through an embedding layer. The specific process is as follows: we obtain word-level feature representations \({E}_{i}^{w}\) through pre-trained word embeddings, and part-of-speech feature representations \({E}_{i}^{t}\) are acquired by mapping words in X to their parts-of-speech via random part-of-speech embeddings. Then, for each character in X , we map its character representation \({E}_{i}^{c}\) using random character embeddings. The formula is as follows:

e w , e t and e c respectively represent the word embedding vector matrix, the part-of-speech embedding vector matrix, and the character embedding vector matrix. x i is the i -th character generated by the text sequence X , and w j and t j are the word and part-of-speech corresponding to x i .

Subsequently, e w , e t , and e c are concatenated along the vector dimensions to obtain a fused multidimensional feature encoding representation \({X}_{i}^{w}\) of the text. This is formally represented as shown in Eq. ( 4 ), where [ \(;\) ] denotes the concatenation operation.

3.2.2 Feature Extraction

We use Bi-LSTM to extract features. Bi-LSTM consists of two LSTM layers. Each LSTM unit contains an input gate, a forget gate, an update gate, and a cell state. Among them, \(\upsigma \) is the Sigmoid function, which can normalize the input value to the range of 0 to 1. i t corresponds to the input gate, which controls the amount of information that should be retained in the cell state update value \(\widetilde{{c}_{t}}\) . f t corresponds to the forget gate, which controls the degree to which the cell state c t-1 should forget information. o t corresponds to the output gate, which controls which information of the current cell state c t is output in the current output. The calculation formula of LSTM is shown below:

In this paper, after obtaining the multidimensional feature vector representation, Bi-LSTM is used to extract multidimensional features to further capture the contextual information of the sequence. The hidden state of Bi-LSTM can be represented as:

where \(\overrightarrow {{H_{i}^{w} }}\) and \(\overleftarrow {{H_{i}^{w} }}\) represent the i -th forward and backward hidden states of Bi-LSTM, and \({H}_{i}^{w}\) is the hidden vector output by Bi-LSTM, representing the semantic representation of the multidimensional features of the i -th token in the input text.

3.3 Fuzzy Localization

We divide nested entity recognition into two sub-tasks: fuzzy localization and entity classification. The flowcharts of the two parts are shown in Fig.  3 (the window size is set to 4). For example, if the window expansion threshold size is 4, considering the sentence “我喜欢北京大学这个学校” after obtaining the feature representation through the shared encoding module, if the Softmax classifier predicts the entity “北京”, by using the window expansion, we extend to the left to include the character “我” and to the right to include the character “个” Through enumeration, multiple candidate entities are obtained, and finally, these candidate entities are classified, resulting in the recognition of “北京” and “北京大学”. The task of the fuzzy localization is to predict the possible positions of entities based on sequence labeling methods. For \(X=\{{x}_{1},{x}_{2}...{x}_{n}\}\) , suppose the labelled entity is recorded as \({x}_{i},{x}_{i+1}...{x}_{j}\) , the label of \({x}_{i}\) is “B”,which is marked as “1”, the label of \({x}_{j}\) is “E”, which is marked as “2”, the internal label of the entity is “I”, which is marked as “3”, and the non-entity label is “O”, which is marked as “0”.

figure 3

Localization and classification flowchart

For \({x}_{i}\) in the text X , the feature representation \({H}_{i}^{w}\) obtained by its corresponding shared encoding (as described in Sect.  3.2.2 ) is input into the RELU activation function and Softmax classifier for prediction labeling, where W t and b t are trainable parameters, as shown in formulas ( 14 ) and ( 15 ):

In the fuzzy localization, the KL divergence multi-label loss between the true distribution \(\widehat{{f}_{i}^{t}}\) and the predicted distribution \({f}_{i}^{t}\) is calculated as shown in formula ( 16 ):

3.4 Entity Classification

The task of the entity classification is to use the labeling list made by the fuzzy localization to identify nested entities and non-nested entities and classify them. It consists of three parts: expanded window, candidate entity representation, and entity classification. Given an input text sequence \(X=\{{x}_{1},{x}_{2}...{x}_{n}\}\) and the corresponding labeling list \(L=[{l}_{1},{l}_{2}...{l}_{n}]\) output by the fuzzy localization, locate the “1” and “2” and the labels composed of “3” in the middle, expand left and right according to the window expansion size to obtain the corresponding sub-sequence, and finally enumerate the candidate entities in the sub-sequence and classify them.

3.4.1 Window Expanding

Given the window expansion size, first traverse the labeling list output by the fuzzy localization to determine the start position of the entity, and then determine the end position of the entity. Then expand the left boundary of the sub-sequence from the start position of the entity according to the window expansion size, and expand the right boundary of the sub-sequence according to the end position of the entity and the window expansion size. Finally, the sub-sequence is obtained according to the expanded left boundary position and right boundary position. This paper does not expand the window for single-character entities. In order to better illustrate the way to expand the window to obtain the sub-sequence, the main steps are summarized in Algorithm 1.

figure a

The method of expanding window to get Sub-sequence.

3.4.2 Candidate Entity Representation

Given an input text sequence \(X=\{{x}_{1},{x}_{2}...{x}_{n}\}\) and the corresponding fuzzy localization labeling list \(L=[{l}_{1},{l}_{2}...{l}_{n}]\) , the sub-sequence obtained by expanding the window is \(D=\{{x}_{q},{x}_{q+1}...{x}_{v}\}\) , where \(q\) > = 1 and \(v\) < =  \(n\) . Then enumerate all start and end positions in the sub-sequence to obtain all candidate entities. If the start position of the candidate entity in the sub-sequence is i and the end position is j , we represents the candidate entity area as the average output of each character’s shared encoding module, and candidate entity \({E}_{i,j}\) is represented as follows:

where \(i\) < =  \(q\) , \(j\) < =  \(v\) , \({H}_{k}^{w}\) represents the output of the shared encoding of the token in the text.

3.4.3 Entity Classification

After obtaining the representation of the sub-sequence and candidate entities expanded according to the window expansion size, the candidate entities are finally classified. This paper uses Softmax to predict entity classification labels:

Among them, \({W}_{i,j}^{e}\) and \({b}_{i,j}^{e}\) are trainable parameters, \({E}_{i,j}\) is the candidate entity representation. In the entity classification, the loss of predicting the classification label is calculated as shown in formula ( 19 ):

where \(\widehat{{g}_{i,j}^{e}}\) and \({g}_{i,j}^{e}\) represent the true distribution and predicted distribution of entity classification labels, respectively.

3.5 Multi-Task Training

In MFFL, due to the detection of some entities firstly and then positioning them before entity classification, error propagation may occur. Considering that the fuzzy localization and the entity classification share the same entity representation, this paper applies multi-task loss to train these two tasks simultaneously. During the training phase, the real label is input into the entity classification so that it can be trained without being affected by the error of the fuzzy localization. In the test phase, the output of fuzzy localization is used to specify which entity areas should be considered by the entity classification. The multi-task loss function is defined as follows:

where \({L}_{p}\) and \({L}_{c}\) represent the cross-entropy loss of the fuzzy localization and the entity classification, respectively. \(\alpha\) is a hyperparameter that controls the weight of each task.

4 Experiments

This section first introduces the basic experimental settings, including the dataset, evaluation metrics, and experimental settings. Then, the advantages of MFFL are verified through comparative experiments on two datasets. Since there are relatively few Chinese nested entity recognition datasets and relatively few models for comparative experiments, this paper not only conducted comparative experiments on Chinese nested entity datasets but also comparative experiments with traditional Chinese non-nested entity recognition models to verify the effectiveness of the proposed model. Finally, ablation experiments were conducted to verify the effectiveness of multi-dimensional features and windows.

4.1 Datasets

We selects two public datasets for experiments, namely the People’s Daily dataset [ 23 ] used to verify nested entity recognition and the Resume dataset used for non-nested named entity recognition. The People’s Daily belongs to the news field, and the number of nested entities accounts for about 12.81% of the total number of entities, including three entity categories, as shown in Table  1 . The Resume dataset comes from Sina Finance and consists of resumes of listed executives, including eight entity categories, as shown in Table  2 .

4.2 Evaluation Indicators

PyTorch is adopted to construct the overall model proposed in this paper. Precision (P), recall(R), and F1 value are used as evaluation metrics, and the calculation formulas are as follows:

where TP represents the number of positive classes predicted as positive c.asses, that is, the number of correct entities identified; FP represents the number of predicted negative classes as positive classes, that is, the number of entities identified with errors; FN stands for predicting positive classes as negative class numbers, that is, the number of unrecognized entities.

4.3 Experimental Setup

The graphics card model used in the experiments is NVIDIA A16. The programming language is Python3.8, and the deep learning framework is PyTorch 1.11.0. The word vector list obtained by training the corpus of Wikipedia with Word2vec tool and a vocabulary of more than 2000k words is used to initialize the semantic word vectors of words in the text, with a dimension of 300. The part-of-speech vector and character vector are obtained by random initialization, and the dimensions are also 300.

During the model training process, the Adam optimizer is used for optimization. The batch size for both the People’s Daily and Resume datasets is set to 32, and the number of iterations (epoch number) is 120. The hyperparameters \(\alpha\) in the joint loss function are set to 0.3. The specific hyperparameter settings are shown in Table  3 .

4.4 Comparative Experiment

The comparative experiments of this section mainly include comparative experiments on Chinese datasets for nested named entity recognition and non-nested named entity recognition. People’s Daily dataset is used to evaluate the performance of the fuzzy localization and the entity classification in Chinese nested entity recognition.

4.4.1 Chinese Nested Named-Entity Recognition

In order to verify the effectiveness of MFFL, this paper selects six representative models related to this paper’s model as comparative models, which are listed as follows:

Introduction to comparative models.

Layered-Bi-LSTM-CRF [ 4 ] was used to detect nested entities in English datasets. Taking the difference between Chinese and English into account, we replace the embedding layer with a structure suitable for Chinese characters. It is a sequence labeling model that proposes a multi-layer CRF model. The model identifies nested entities of different levels from the inside out.

Ex_Model [ 7 ] used a sequence labeling model to detect nested entity boundaries, merged the corresponding boundary label sequence, and completed classification prediction.

Bo_Model [ 8 ] is based on a span-based approach, which aims to locate entity boundaries and jointly learn boundary detection and entity classification tasks.

Bert-MRC [ 11 ] regarded the entity recognition task as a machine reading comprehension task and formalized entity extraction as extracting the answer span of the question mentioned in the text.

SAAM [ 23 ] used a semi-automatic labeling method and multiple CRF models to label multi-layer nested entities.

Bottom-Up Parsing [ 24 ] generated shared span boundaries by subsequent traversal of phrase structure trees, and then used a pointer network to track shared boundaries to train models to predict unknown nested entity boundaries.

Comparison of experimental results.

The experimental results of Layered-Bi-LSTM-CRF, Exhaustive Model, Bert-MRC, and Bottom-Up Parsing were all reproduced using the People’s Daily dataset. I_MFFL and W_MFFL are the model results of randomly initialized word vectors and word vectors obtained using Word2vec in this paper. The specific comparison results are shown in Table 4 .

From the above comparison results, it can be seen that the F1 value of W_MFFL (MFFL) is the best, followed by Bottom-Up Parsing. Layered-Bi-LSTM-CRF utilizes the encoding information of internal named entities to identify external named entities efficiently and dynamically adjusts the number of layers in the flat NER based on the nesting depth. However, the model structure is relatively complex, which may require significant computational resources and longer training times. In contrast, our model can locate nested entities by utilizing both internal and external named entities, employs multitask learning to alleviate error propagation issues, and effectively learns the correlation between fuzzy positioning and classification labels. The model in Ex_Model and Bo_Model are based on span-based methods. Ex_Model suffers from considering excessive irrelevant regions (non-entity regions), resulting in significant computational and memory overheads. In contrast, our model utilizes fuzzy positioning to filter out excessive irrelevant regions, reducing time complexity. Bo_Model focuses on boundary detection, but the boundaries in Chinese are often ambiguous, making nested entity recognition challenging. Our model leverages the dependency between internal and external entities to better utilize nested entity features.

For this paper model, compared with I_MFFL, W_MFFL has improved the F1 value by 0.36 percentage points, which indicates that the model method of obtaining word vectors using Word2vec is better than the model method of randomly initializing word vectors. Overall, W_MFFL has improved compared to the seven models mentioned above (including I_MFFL), which proves that MFFL is indeed effective in nested entity recognition.

Fuzzy localization performance results.

The fuzzy localization and the entity classification are closely related and have a significant impact on entity recognition. Therefore, this paper also evaluated the performance of the fuzzy localization, and the experimental results show that the performance of fuzzy localization does indeed affect the results of entity classification. This paper uses the People’s Daily dataset to evaluate the performance of the fuzzy localization. “0”, “1”, “2” and “3” respectively represent the tags that are not entities, entity heads, entity tails and entity centers. The results are based on shared encoding and Softmax classifiers to perform fuzzy localization of entities with relatively high performance. The following Table  5 shows the experimental results on the test dataset:

Entity classification performance results.

The entity classification is a performance test of the entity recognition effect, and the results of each category affect the experimental results. The results are based on shared encoding, fuzzy localization and Softmax classifiers to perform entity recognition with relatively high performance. This paper uses the People’s Daily dataset to evaluate the performance of entity recognition. The following Table  6 shows the performance on the PER, LOC and ORG categories in the test dataset. To demonstrate the performance of our proposed model, we compared the F1 scores of entity classification with those of Ex_Model [ 7 ] and Bo_Model [ 8 ]. The results indicate that our model outperforms the entity classification performance of the other two models.

4.4.2 Chinese Non-Nested Named Entity Recognition

In order to prove that the model in this paper also has advantages in identifying non-nested entities, this paper conducted comparative experiments with the following models on the Resume dataset. The experimental results are shown in Table  7 :

Lattice and LRCNN are models based on lexical enhancement. These two models are implemented using LSTM and CNN methods, respectively. SoftLexicon (LSTM)-BERT is used to add position information of a character in different vocabulary. MFFL has the best F1 value effect on the Resume dataset, followed by SoftLexicon(LSTM)-BERT, and MFFL is 0.14 percentage points higher than SoftLexicon (LSTM)-BERT. Overall, MFFL has improved compared to the above three models, which shows that the multi-dimensional feature fusion method proposed in this paper can indeed effectively enhance the text features to obtain comprehensive semantic information. Combining the fuzzy localization can accurately divide entities. The two comparative experiments of Chinese nested entity recognition and traditional named entity recognition have proved that MFFL is not only effective in nested entity recognition, but also improves the performance of Chinese non-nested named entity recognition.

4.5 Ablation Experiment

We conducted an ablation study on the People’s Daily dataset. We evaluated the contributions of three features: characters, words, and parts of speech to the effect, as shown in Table 8 . We also verified the effect of window expansion size on running time, as shown in Table 9 .

4.5.1 Effect of Feature Factors

In order to verify that the three features of characters, words, and parts of speech are helpful for feature extraction, this section will test different combinations of different features mentioned in this paper to verify their effectiveness. The following is an evaluation of the performance of the test dataset in the People’s Daily dataset with a window expansion size of 4. In Table  8 below, MFFL1 only contains character features, MFFL2 contains character and word features, and MFFL is the multi-dimensional feature model method proposed in this paper that includes character, word and part-of-speech features.

The experimental results show that when the word feature is added to the model, the F1 value of MFFL2 compared with MFFL1 is only increased by 1.07 percentage points, and the increase is relatively small. This indicates that considering only character features or considering both character and word features does not have a significant impact on solving the problem of nested entities. However, when the three features of characters, words, and parts of speech are applied to MFFL at the same time, the F1 value is increased by 3.93 and 5 percentage points compared with MFFL2 and MFFL1, respectively. This indicates that considering these three features at the same time has the greatest impact on entity recognition. In addition, this ablation experiment also confirms that the three features of characters, words, and parts of speech have an impact on feature extraction, but the impact of part-of-speech features on model performance is the most significant.

4.5.2 Effect of Window Expanding

In order to verify the impact of the window on performance and time, the experimental results are shown in Table  9 . “MFFL_2”, “MFFL_4”, “MFFL_6”, “MFFL” and “MFFL_10” are the experimental results with window expansion sizes of 2, 4, 6, 8 and 10 respectively. “MFFL_N” is the experimental result without window expansion, enumerating all regions with Span_max not exceeding 20.

The experimental results show that MFFL_6 and MFFL_4 have increased the F1 value by 0.6 percentage points compared to MFFL_N; Compared to MLNER, MLNER_6 shows an increase of 0.77 percentage points in F1 score. However, MLNER_10 demonstrates a decrease in F1 score compared to MLNER. This suggests that, under the current dataset or experimental conditions, the model with a window size of 8 has already effectively utilized the contextual information of surrounding text. Further increasing the window size may lead to excessive capture of irrelevant information, thereby reducing the model's generalization ability and impacting performance. And without window expansion, MFFL_N has the same F1 value as MFFL, and the three evaluation indicators are almost the same. This indicates that as the window expansion size increases, the F1 value will also be improved, and time will also be affected by the window expansion size. Although the performance without window expansion is relatively high, the time is also relatively long. This ablation experiment proves that compared with adding a window and not adding a window, although it cannot improve accuracy, it can indeed reduce computational costs and time costs.

5 Conclusions

To address the present issues in traditional Chinese nested entity recognition methods, we proposed a nested entity recognition method based on Multidimensional Features and Fuzzy Localization (MFFL). The core idea is to first perform fuzzy localization and then execute precise classification. Specifically, the text's multidimensional feature information is initially encoded and fused, followed by utilizing the fuzzy localization module to pinpoint potential entity locations. Subsequently, windows are employed to expand sub-sequences. Finally, the candidate entities enumerated from these sub-sequences are classified. This strategy effectively integrates external information to enhance the precision of predictions and employs entity location information to reduce time and computational costs. Finally, experiments conducted on two publicly available datasets demonstrated that MFFL outperforms several classical models in both nested named entity recognition and traditional named entity recognition tasks. For future work, there will be an attempt to integrate label information of partially recognized entities within the fuzzy localization. Through this approach, it is anticipated that entity information can be utilized more comprehensively and effectively, thereby further optimizing the model's capability in capturing dependencies among entities.

Data Availability

Not applicable.

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Thank you very much to everyone who contributed to the paper.

Authors’ contributions

Hua Zhao: Conceptualization, Methodology, Software, Investigation, Formal Analysis, Writing—Original Draft; Xueyang Bai: Data Curation, Writing—Original Draft; Qingtian Zeng: Overall Planner and Regulatory; Heng Zhou: Auxiliary code writing and data collection; Xuemei Bai: Software, Validation.

This research was supported by the National Key R&D Program of China (2022ZD0119500); Shandong Natural Science Foundation Project (No. ZR2021MG038); Special Study on Cultural Tourism of Shandong Social Science Planning (No. 21CLYJ32); Shandong Postgraduate Education Quality Improvement Plan (No. SDYJG19075); Education Quality Improvement Plan (No.SDYJG19075); Shandong Education Teaching Research Key Project (No.2021JXZ010); National Statistical Science Research Project (No.2021LY053).

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1、Hua Zhao:Conceptualization, Methodology, Software, Investigation, Formal Analysis, Writing - Original Draft; 2、Xueyang Bai:Data Curation, Writing - Original Draft; 3、Qingtian Zeng:Overall Planner and Regulatory; 4、Heng Zhou:Auxiliary code writing and data collection; 5、Xuemei Bai:Software, Validation.

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Zhao, H., Bai, X., Zeng, Q. et al. Nested Entity Recognition Method Based on Multidimensional Features and Fuzzy Localization. Neural Process Lett 56 , 196 (2024).

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Research Article

Automated free speech analysis reveals distinct markers of Alzheimer’s and frontotemporal dementia

Contributed equally to this work with: Pamela Lopes da Cunha, Fabián Ruiz

Roles Data curation, Formal analysis, Investigation, Writing – original draft

Affiliations Cognitive Neuroscience Center, Universidad de San Andrés, Victoria, Buenos Aires, Argentina, Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Ciudad Autónoma de Buenos Aires, Buenos Aires, Argentina

Roles Data curation, Formal analysis, Methodology, Writing – review & editing

Affiliation Cognitive Neuroscience Center, Universidad de San Andrés, Victoria, Buenos Aires, Argentina

Roles Data curation, Formal analysis, Writing – review & editing

Affiliations Cognitive Neuroscience Center, Universidad de San Andrés, Victoria, Buenos Aires, Argentina, Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Ciudad Autónoma de Buenos Aires, Buenos Aires, Argentina, Facultad de Ingeniería, Universidad de Buenos Aires (FIUBA), Ciudad Autónoma de Buenos Aires, Buenos Aires, Argentina

Roles Methodology, Writing – review & editing

Roles Funding acquisition, Writing – review & editing

Affiliations Cognitive Neuroscience Center, Universidad de San Andrés, Victoria, Buenos Aires, Argentina, Latin American Brain Health (BrainLat) Institute, Universidad Adolfo Ibáñez, Santiago, Peñalolén, Región Metropolitana, Chile, Global Brain Health Institute, University of California San Francisco, San Francisco, California, United States of America, Trinity College Dublin, Dublin, Ireland

Roles Resources, Writing – review & editing

Affiliations Faculty of Medicine, Neuroscience and East Neuroscience Departments, Neuropsychology and Clinical Neuroscience Laboratory (LANNEC), Physiopathology Program – Institute of Biomedical Sciences (ICBM), University of Chile, Santiago, Chile, Geroscience Center for Brain Health and Metabolism (GERO), Providencia, Santiago, Chile, Hospital del Salvador and Faculty of Medicine, Memory and Neuropsychiatric Center (CMYN), Neurology Department, University of Chile, Providencia, Santiago, Chile, Departamento de Medicina, Servicio de Neurología, Clínica Alemana-Universidad del Desarrollo, Las Condes, Región Metropolitana, Chile

Affiliations Facultad de Medicina, Departamento de Psiquiatría (Programa PhD Neurociencias), Instituto de Envejecimiento, Pontificia Universidad Javeriana, Bogotá, Colombia, Centro de Memoria y Cognición, Intellectus, Hospital Universitario San Ignacio Bogotá, San Ignacio, Colombia, Departamento de Salud Mental, Hospital Universitario Santa Fe de Bogotá, Bogotá, Colombia

Roles Data curation, Resources, Writing – review & editing

Affiliation Escuela de Medicina y Ciencias de la Salud, Universidad del Rosario, Bogotá, Colombia

Roles Formal analysis, Writing – review & editing

Affiliations Cognitive Neuroscience Center, Universidad de San Andrés, Victoria, Buenos Aires, Argentina, Departamento de Matemática, Universidad de San Andres, Victoria, Buenos Aires, Argentina

Roles Conceptualization, Funding acquisition, Methodology, Project administration, Supervision, Writing – review & editing

* E-mail: [email protected]

Affiliations Cognitive Neuroscience Center, Universidad de San Andrés, Victoria, Buenos Aires, Argentina, Latin American Brain Health (BrainLat) Institute, Universidad Adolfo Ibáñez, Santiago, Peñalolén, Región Metropolitana, Chile, Global Brain Health Institute, University of California San Francisco, San Francisco, California, United States of America, Facultad de Humanidades, Departamento de Lingüística y Literatura, Universidad de Santiago de Chile, Estación Central, Santiago, Chile

ORCID logo

  • Pamela Lopes da Cunha, 
  • Fabián Ruiz, 
  • Franco Ferrante, 
  • Lucas Federico Sterpin, 
  • Agustín Ibáñez, 
  • Andrea Slachevsky, 
  • Diana Matallana, 
  • Ángela Martínez, 
  • Eugenia Hesse, 
  • Adolfo M. García


  • Published: June 6, 2024
  • Reader Comments

Fig 1

Dementia can disrupt how people experience and describe events as well as their own role in them. Alzheimer’s disease (AD) compromises the processing of entities expressed by nouns, while behavioral variant frontotemporal dementia (bvFTD) entails a depersonalized perspective with increased third-person references. Yet, no study has examined whether these patterns can be captured in connected speech via natural language processing tools. To tackle such gaps, we asked 96 participants (32 AD patients, 32 bvFTD patients, 32 healthy controls) to narrate a typical day of their lives and calculated the proportion of nouns, verbs, and first- or third-person markers (via part-of-speech and morphological tagging). We also extracted objective properties (frequency, phonological neighborhood, length, semantic variability) from each content word. In our main study (with 21 AD patients, 21 bvFTD patients, and 21 healthy controls), we used inferential statistics and machine learning for group-level and subject-level discrimination. The above linguistic features were correlated with patients’ scores in tests of general cognitive status and executive functions. We found that, compared with HCs, (i) AD (but not bvFTD) patients produced significantly fewer nouns, (ii) bvFTD (but not AD) patients used significantly more third-person markers, and (iii) both patient groups produced more frequent words. Machine learning analyses showed that these features identified individuals with AD and bvFTD (AUC = 0.71). A generalizability test, with a model trained on the entire main study sample and tested on hold-out samples (11 AD patients, 11 bvFTD patients, 11 healthy controls), showed even better performance, with AUCs of 0.76 and 0.83 for AD and bvFTD, respectively. No linguistic feature was significantly correlated with cognitive test scores in either patient group. These results suggest that specific cognitive traits of each disorder can be captured automatically in connected speech, favoring interpretability for enhanced syndrome characterization, diagnosis, and monitoring.

Citation: Lopes da Cunha P, Ruiz F, Ferrante F, Sterpin LF, Ibáñez A, Slachevsky A, et al. (2024) Automated free speech analysis reveals distinct markers of Alzheimer’s and frontotemporal dementia. PLoS ONE 19(6): e0304272.

Editor: Lorenzo Pini, University of Padova: Universita degli Studi di Padova, ITALY

Received: January 9, 2024; Accepted: May 9, 2024; Published: June 6, 2024

Copyright: © 2024 Lopes da Cunha et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All data and preprocessing/analysis code are available in the following Open Science Framework repository: .

Funding: AG is an Atlantic Fellow at the Global Brain Health Institute (GBHI; ) and is partially supported with funding from the National Institute On Aging of the National Institutes of Health (NIA-NIH; ; R01AG075775, 2P01AG019724-21A1); Agencia Nacional de Investigación y Desarrollo (ANID; ; FONDECYT Regular 1210176, 1210195); GBHI, Alzheimer’s Association, and Alzheimer’s Society (Alzheimer’s Association GBHI ALZ UK-22-865742); the Latin American Brain Health Institute (BrainLat; ), Universidad Adolfo Ibáñez, Santiago, Chile (#BL-SRGP2021-01); Programa Interdisciplinario de Investigación Experimental en Comunicación y Cognición (PIIECC; ), Facultad de Humanidades, Universidad de Santiago de Chile. AI is partially supported by grants ANID/FONDECYT Regular (1210195 and 1210176 and 1220995); ANID/FONDAP/15150012; ANID/PIA/ANILLOS ACT210096; ANID/FONDEF ID20I10152 and ID22I10029; Takeda ( ) CW2680521 and the MULTI-PARTNER CONSORTIUM TO EXPAND DEMENTIA RESEARCH IN LATIN AMERICA [ReDLat; ; supported by National Institutes of Health, National Institutes of Aging (R01 AG057234), Alzheimer’s Association (SG-20-725707), Rainwater Charitable foundation – Tau Consortium, and Global Brain Health Institute)]. AS is supported by ANID (FONDAP ID15150012); Fondecyt Regular 1231839 and PIA Anillos ACT210096) & MULTI-PARTNER CONSORTIUM TO EXPAND DEMENTIA RESEARCH IN LATIN AMERICA [ReDLat, supported by National Institutes of Health, National Institutes of Aging (R01 AG057234), Alzheimer’s Association (SG-20-725707), Rainwater Charitable foundation – Tau Consortium, and Global Brain Health Institute)]. The contents of this publication are solely the responsibility of the authors and do not represent the official views of these Institutions. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.


Affecting nearly 55 million people worldwide, Alzheimer’s disease (AD) and behavioral variant frontotemporal dementia (bvFTD) are the most prevalent forms of dementia [ 1 , 2 ]. These syndromes differ in neurological and clinical aspects. AD typically entails temporo-parieto-hippocampal atrophy, progressive semantic and episodic memory deficits, and executive function declines [ 3 ]. Conversely, bvFTD involves fronto-insulo-temporal atrophy and social behavior changes such as disinhibition, apathy, compulsion, and impaired moral judgment [ 4 ]. Yet, their clinical differentiation in early stages is challenging due to several cognitive and behavioral overlaps [ 3 , 5 ]. From a linguistic perspective, these include difficulties with understanding and recounting daily situations, which patients from both populations narrate in a disorganized [ 6 ] and uninformative [ 7 , 8 ] fashion. Here, we investigate whether clinically motivated natural language processing (NLP) features can capture differential markers of each disorder.

Previous works have revealed connected speech alterations in both syndromes. People with AD have difficulties maintaining referential [ 9 , 10 ] and temporal [ 6 ] cohesion, which can affect global coherence. Also, they show reduced idea density and lexical diversity [ 11 , 12 ], leading to uninformative speech [ 13 ]. Early impairment in lexico-semantic abilities can also lead to word-finding delay [ 14 , 15 ], semantic paraphasias [ 14 , 16 ], and naming difficulties [ 17 , 18 ]. For their part, people with bvFTD exhibit reduced propositional content [ 7 ], poor idea organization [ 19 ], and increased superfluous content [ 20 , 21 ], alongside possible morphosyntactic deficits manifested as challenges in complex sentence comprehension and completion [ 22 ] and difficulty sequencing events [ 23 ]. Yet, linguistic markers have rarely been jointly examined in both populations, let alone by testing clinically grounded hypotheses in naturalistic routine descriptions. Such is the focus of this paper.

Daily situations are construed by identifying people, objects or other entities (expressed through nouns) engaged in actions or inner experiences (expressed through verbs) from an egocentric or exocentric perspective (via first or third person references) [ 24 ]. Persons with AD have been shown to produce fewer nouns (but not fewer verbs) than HCs during interviews, including questions about their experiences [ 25 – 27 ]. Suggestively, the same occurs in semantic dementia (another syndrome with primary semantic memory deficits) [ 28 ], but not in bvFTD [ 29 ]. This pattern aligns with models that propose that nouns are differentially subserved by temporal and temporo-parietal circuits [ 30 , 31 ] which are differentially affected in AD [ 32 – 34 ], while verbs would be critically underpinned by frontal/motor areas [ 30 , 35 , 36 ]. Conversely, bvFTD may involve abnormal perspective taking in social scenarios [ 37 ]. Patients are typified by inaccurate self-awareness and self-monitoring [ 38 ], and they favor a third-person perspective for self-representation [ 39 ]. Indeed, self-referential processing recruits prefrontal regions distinctly compromised in bvFTD [ 40 ]. These patterns, we surmise, may be reflected in a preference for third- over first-person pronouns in connected speech. In addition, evidence from more controlled tasks, such as verbal fluency, suggests that these syndromes may differ in their vocabulary navigation patterns, with AD (but not bvFTD) patients differing from HCs in using more frequent and otherwise more accessible words [ 41 ]. In sum, AD and bvFTD might be typified by specific anomalies in their linguistic expression of daily events.

Simple NLP tools are well-suited to test this conjecture. This approach can improve the characterization, diagnosis, and phenotyping of different neurodegenerative diseases [ 13 ], while having the potential to foster global equity in the fight against dementia [ 42 ]. More particularly, they can yield different insights into neurodegenerative disorders depending on the type of linguistic task analyzed. For instance, semi-spontaneous tasks, such as picture descriptions, seem well suited to evaluate semantic memory [ 13 , 43 ]. For their part, spontaneous tasks, such as unstructured interviews, better capture natural discourse profiles in terms of syntactic structure, coherence, and cohesion [ 9 ]. Among the latter, routine description tasks are useful to target the present study’s features, given their focus on objects and actions (typically described through nouns and verbs) that can be described from ego-centric or exo-centric perspectives.

Part-of-speech tagging tools can automatically identify nouns and distinguish them from other categories (e.g., verbs) [ 44 ]. Likewise, morphological tagging tools can discriminate between those coding egocentric (i.e., first-person markers, such as I and my ) and exocentric reference (e.g., third-person markers, such as she and her ) [ 44 ]. Also, word properties can be derived through fully automated algorithms [ 41 ]. Promisingly, given their simplicity, automaticity, and affordability, these tools could be leveraged in diverse clinical settings. However, despite the growth of NLP in dementia research [ 13 , 29 , 45 ], no study has tested this potential double dissociation, let alone combining inferential statistics and machine learning tools for group- and subject-level discrimination, respectively.

Here we examined whether persons with AD and bvFTD differ from HCs in their linguistic expression of daily events. We recorded participants as they described a day in their lives, transcribed their speech, and automatically calculated the proportion of nouns, verbs, first-person markers, and third-person markers ( Fig 1 ). We performed a main study on a subset of participants, and reserved part of our sample for a generalizability test. First, we predicted that persons with AD, unlike those with bvFTD, would be selectively impaired in noun (but not verb) retrieval. Second, we hypothesized that persons with bvFTD, but not AD, would rely less on first-person reference and more on third-person reference. Third, we hypothesized that AD patients, unlike bvFTD patients, would employ more frequent words. Fourth, we anticipated that these features would offer good subject-level classification and robust generalizability to unseen samples in machine learning analyses. Finally, we explored whether these features were associated with overall cognitive impairment in each group. Briefly, with this approach, we aim to reveal novel automated markers of the two most prevalent forms of dementia.


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(A) Ninety-six subjects (32 with AD, 32 with bvFTD, and 32 healthy controls) performed a (B) routine description task. (C) Transcripts from their recordings were analyzed to examine predicted differences in their linguistic expression of situations, with a focus on (i) noun and verb ratios, derived through part-of-speech tagging; (ii) first- and third-person ratios, obtained via morphological tagging; and (iii) word properties (frequency, phonological neighborhood, length, semantic variability), captured through the TELL app. AD: Alzheimer’s disease; bvFTD: Behavioral variant frontotemporal dementia; NLP: Natural language processing; POS: Part of speech.

Materials and methods


The study involved 96 native Spanish speakers, recruited in two centers from the ReDLat consortium. Our main analyses comprised 21 persons with AD, 21 persons with bvFTD, and 21 HCs ( Fig 1A ), reaching a power of 0.81 ( S1 File ). The remaining participants (11 with AD, 11 with bvFTD, 11 HCs) were used as a hold-out sample to test for generalizability in our machine learning analyses ( S2 File ). Patients were diagnosed by expert neurologists following NINCDS-ADRDA criteria for AD [ 46 ] and current clinical criteria for probable bvFTD [ 4 ]. Diagnoses were supported by an extensive neurological, neuropsychiatric, and neuropsychological examination [ 47 ] following unified procedures [ 48 ]. Persons with AD presented memory impairment and individuals with bvFTD exhibited socio-behavioral impairments verified by caregivers. Both groups showed general cognitive deficits, based on the Montreal Cognitive Assessment (MoCA) [ 49 ]; and executive dysfunction, as established through the INECO Frontal Screening (IFS) battery [ 50 ]. No patient reported a history of other neurological disorders, and none had primary linguistic deficits (as established through a neuropsychological interview, caregiver reports, and qualitative evaluation of conversational speech). HCs were cognitively preserved and functionally autonomous. None reported a background of neuropsychiatric disease or substance abuse. All participants had a normal or corrected-to-normal hearing, determined through a formal functionality survey. Each patient group was matched with HCs in terms of sex, age, and education. Demographic and neuropsychological details are shown in Table 1 .


Recruitment for this study took place between May 11, 2021, and June 4, 2022. All participants provided written informed consent, documented by a researcher. The study was performed according to the Declaration of Helsinki and approved by the Ethics’ Committee of Universidad de Chile.

Speech elicitation and transcription

Speech was elicited at the clinicians’ offices, after the neuropsychological evaluation. Participants were invited to describe a typical day in their lives ( Fig 1B ), since they woke up until they went to bed, with the following instruction: “Now you will describe your typical day in your life. Please describe everything you do since you wake up until you go to bed at night. Use as much detail as possible. For example, instead of saying ‘I make breakfast,’ tell me everything you do to make breakfast. Are you ready? Please speak at your usual speed, pitch, and volume.” Examiners were instructed to elicit between 1 and 2 minutes of speech from each participant. If a participant stopped talking before the 1-minute mark, examiners prompted them to continue speaking by saying “Tell me more.” Narrations were audio-recorded with high-end cell phones (sampling rate = 44.1 Hz, resolution = 16 bits), and saved as.wav files.

Audio-recordings were transcribed verbatim with Google speech-to-text software. Transcriptions were revised manually by Spanish-speaking neuropsychologists who were blind to group- and protocol-specific information. They were all specialized in language testing and followed reported procedures [ 51 ] including use of standard punctuation norms from Royal Spanish Academy ( ) and allocation of full stops based on grammatical criteria. The very few instances of unintelligible words were discarded from the transcripts and analyses. Prior to feature extraction, all transcripts were tokenized (i.e., divided into smaller units apt for NLP and machine learning analysis) using Freeling 4.2 [ 44 ], without removing stop words (textual elements other than content words, including dates, punctuation markers, determiners, adpositions, conjunctions, interjections, numbers). Texts were not lemmatized (i.e., converted to their base form upon removing inflectional morphemes), as this would impede the identification of first- and third-person markers. Filled pauses, hesitations, and false starts were transcribed fully, even though they were excluded from analyses as our hypotheses focused on specific word categories. Importantly, transcribed strings that did not represent full words were omitted from analysis even if they belonged to our categories of interest—e.g., an interrupted noun, such as ‘ hospi ’ in After lunch , I go to the hospi… rather , to the clinic .

Feature extraction

Automated estimation of word class usage..

Nouns and verbs ( Fig 1C ) were automatically identified via FreeLing’s POS-tagger [ 44 ]. Specifically, based on a standard trigram hidden Markov model, it replaces each token by its lexical category (namely, nouns, verbs, adjectives, adverbs, determiners, pronouns, conjunctions and adpositions)–a context-sensitive task that FreeLing achieves with an accuracy of 95% [ 44 ]. The ratios of nouns and verbs per participant were calculated by reference to the total number of words, including stop words.

Automated estimation of person usage.

First- and third-person usage ( Fig 1C ) was coded via FreeLing’s morphological tagging module [ 51 ]. This function assigns POS-specific morphological attributes to each token based on grammatical and/or semantic attributes of neighboring words, reaching an accuracy of 95% [ 44 ]. In particular, verbs and pronouns mark for the attribute ‘person’, which can take only one of three possible values: first, second, or third person (e.g., camino , caminas , camina [ I walk , you walk , s/he walks , respectively]). For example, situations may be expressed through first- or third-person references, respectively signaled in bold and in underlined bold case in the next examples from our corpus.

  • La señora se levant a primero , después voy yo .
  • [ The lady gets up first , and then I do so ]
  • Mi hijo lleg a y entonces me dej a las llaves y dic e que si quier o me dej a las puertas abiertas .
  • [ My son arrives and then he gives me the keys and tells me that , if I want , he will open the doors for me ]

Of note, Spanish morphology and syntax offer explicit markers of these properties, as grammatical persons are unambiguously conveyed by the endings of inflected verbs in most tenses (e.g., first person is exclusively conveyed through the ending -o in present (e.g., camino ), -é in past simple (e.g., caminé ), and -aré in future simple (e.g., caminaré ). Moreover, cases of morphological ambiguity (e.g., the imperfect past ending -aba , used for both first and third person) are resolved by intra-sentential person-verb agreement or inter-sentential reference (e.g., Yo siempre caminaba , where yo disambiguates -aba as a first-person marker).

Given the nature of the task and the focus of our hypothesis, we discarded all words with a second-person tag (these amounted to only 1.1% of all person-marked words in our corpus). The ratios of first- and third-person morphemes per participant were calculated by reference to the total number of person occurrences. Pronoun and verb morphemes related to the same event were considered separately (e.g., in the clause Yo salgo , the two first-person markers are counted individually towards our overall first-person marker ratio)–further considerations on this point are offered in the “Discussion” section.

Automated calculation of word properties.

We used a novel automated pipeline [ 41 , 52 ], implemented in the TELL app [ 53 ], to capture objective lexico-semantic features across all content words (nouns, verbs, adjectives, adverbs) from each participant. We extracted four features from each content word ( Fig 1C ). Three of them were obtained through the EsPal database [ 54 ], namely: word frequency (logarithmic frequency per million), phonological neighborhood (number of words obtained by substituting, adding, or omitting a phoneme), and length (number of phonemes). The fourth was an NLP feature called semantic variability [ 41 , 52 , 55 ]. Based on a FastText model pre-trained with language-specific corpora, each text is mapped as a series of vectors, keeping the words’ sequence and omitting repetitions. Distances between adjacent vectors are stored into a time series. Semantic variability is computed as the variance of the text’s joint time series. When semantic distance across adjacent words is inconsistent, the text has high semantic variability.

Quality check.

To ensure that the labels underlying all calculations were adequate, we asked a trained Spanish-speaking psychologist, specialized in language research, to perform a manual revision of all tags from 25% of the transcriptions in each group within our main study. The process showed that automated tags had an accuracy of 90.1%.

Statistical analysis

Group-level comparisons via inferential statistics..

Statistical comparisons were performed between subjects with AD and HCs, and between subjects with bvFTD and HCs, for two dependent variables: word class usage (noun ratio, verb ratio) and person usage (first-person ratio, third-person ratio). In each case, we performed 2x2 mixed ANOVAs, with group as a between-subjects factors (patients, HCs) and tag ratio as a within-subject factor (nouns and verbs, in the case of word class; first and third person, in the case of person usage). Post hoc comparisons were made through Tukey’s HSD tests. Moreover, each word property (frequency, phonological neighborhood, length, semantic variability) was compared between groups via a separate one-tailed independent samples t -tests. Alpha levels were set at p < 0.05. No participant was detected as an outlier in any dataset (at a threshold of 3 SD s from the sample’s mean). All results were corrected for multiple comparisons via the false discovery rate (FDR) metric. Analyses were run on Python 3 (via Pandas 1.3.2 and Pingouin 0.5.1 packages).

Subject-level classification via machine learning analysis.

We utilized a support vector machine (SVM) classifier with a linear kernel to discriminate between patients in each group from HCs. This method models probabilities based on a decision boundary that maximizes the margin between the classes [ 56 ], yielding robust results in language and neuropsychological research on dementia [ 57 ]. Each classifier was trained using all linguistic features. The data were randomly split into five folds for stratified cross-validation, preserving the proportion of labels per group [ 58 ], where four folds were used for training and one fold was used for testing. The values of each feature were normalized using the min-max method [ 59 ]. AUC, accuracy, precision, recall, F1, and UAR scores were reported as mean and SD upon 1000 iterations with different random data partitions. Also, we calculated the contribution of each feature to overall classification, considering the absolute values of each feature’s coefficient in a feature importance analysis. All analyses were performed using Python 3.9 and the Scikit-learn ( ) package. ROC curve plots and feature importance graphs were created using Seaborn Python’s library [ 60 ] and Ggplot R’s library [ 61 ].

Generalizability tests.

To test the generalizability of the machine learning models, we replicated our approach by (i) training each binary classifier (AD patients vs. HCs; bvFTD patients vs. HCs) with all participants from our main study and then (ii) testing them on a hold-out set composed of different participants (11 AD patients, 11 bvFTD patients, 11 HCs). These new groups were socio-demographically matched with each other and relative to the participants of the main study ( S2 File ). This analysis employed the same pipeline of the machine learning analysis in our main study.

Correlations between connected speech features and cognitive status.

We examined whether our target features were associated with patients’ cognitive status, as captured by the MoCA and the IFS battery. Correlations were based on Pearson’s coefficients, corrected for multiple comparisons via FDR, and performed on GraphPad Prism ® (v 6.01).

Speech elicitation

The mean number of words produced by HCs (304.14) did not differ significantly from that of individuals with AD (238.57; t (40) = 1.28, p = 0.21, d = 0.40) or bvFTD (253.95; t (40) = 1.03, p = 0.31, d = 0.32). Instances of unintelligible words, due to recording issues and/or speech errors, were discarded from the transcripts and analyses. These represented fewer than 0.01% of words in each group, and they did not differ significantly between AD patients and HCs ( t = 0.8297, p = 0.41) or between bvFTD patients and HCs ( t = 0.4515, p = 0.65).

Word class usage

The comparison between subjects with AD and HCs ( Fig 2A ) revealed a non-significant effect of group [ F (1,40) = 3.99, p FDR = 0.125, np 2 = 0.09] and a significant effect of word class [ F (1,40) = 30.89, p FDR = 0.001, np 2 = 0.43]. Crucially, the interaction between both factors was significant [ F (1,40) = 6.97, p FDR = 0.024; np 2 = 0.15]. Post hoc analysis, via Tukey’s HSD test ( df = 40, MSE = 0.014), showed that, relative to HCs, persons with AD produced fewer nouns (pFDR = 0.024, d = 1.145) and a similar proportion of verbs ( p FDR = 0.990, d = -0.95). Also, while the proportion of both word classes was similar in HCs ( p FDR = 0.240, d = 0.65), persons with AD produced fewer nouns than verbs ( p FDR = 0.008, d = 1.64).


(A) Noun ratio was lower for AD (but not bvFTD) patients compared with HCs. No between-group differences emerged for verb ratio. (B) BvFTD (but not AD) patients produced significantly fewer first-person and more third-person markers than HCs. (C) Both patient groups produced more frequent content words than did HCs, there being no between-group differences in other word properties (phonological neighborhood, length, semantic variability). In all cases the mean is indicated by the cross. For ease of visualization, brackets show only significant pairwise comparisons from significant interaction effects, performed via Tukey’s HSD test. The number of asterisks denote the alpha threshold of the effect (* = p < 0.05, ** = p < 0.01, *** = p < 0.001). AD: Alzheimer’s disease; bvFTD: Behavioral variant frontotemporal dementia.

As regards the comparison between subjects with bvFTD and HCs ( Fig 2A ), we found a significant group effect [ F (1,40) = 8.98, p FDR = 0.020, np 2 = 0.18], indicating fewer items (regardless of word class) in the patients. We also found a significant word class effect [ F (1,40) = 12.98, p FDR = 0.001, np 2 = 0.25], revealing a lower ratio of nouns than verbs across groups. The interaction between both factors was not significant [ F (1,40) = 1.42, p FDR = 0.240, np 2 = 0.03]. For full details, see S3 File .

Person usage

The comparison between subjects with AD and HCs ( Fig 2B ) yielded a significant effect of person [ F (1,40) = 20.79, p FDR = 0.001, np 2 = 0.34], with a lower proportion of third-person than first-person items across groups. Neither the main effect of group [ F (1,40) = 2.82, p FDR = 0.202, np 2 = 0.07] nor the interaction between group and person [ F (1,40) = 3.43, p FDR = 0.098, np 2 = 0.08] were significant.

Conversely, comparisons between subjects with bvFTD and HCs ( Fig 2B ) revealed a non-significant effect of group [ F (1,40) = 0.06, p FDR = 0.815, np 2 = 0.002] and a significant effect of person [ F (1,40) = 6.78, p FDR = 0.013, np 2 = 0.14]. Crucially, a significant interaction emerged between both factors [ F (1,40) = 8.77, p FDR = 0.020, np 2 = 0.18]. Post hoc analysis, via Tukey’s HSD test ( df = 40, MSE = 0.451), revealed that, relative to HCs, persons with bvFTD produced a significantly lower proportion of first-person items ( p FDR = 0.042, d = -0.91) and a significantly greater proportion of third-person items ( p FDR = 0.042, d = 0.915). Also, HCs relied significantly more on first- than third-person items ( p FDR = 0.008, d = -2.239), but no such difference was observed in persons with bvFTD ( p FDR = 0.999, d = 0.093). For full details, see S3 File .

Word properties

Word property analyses revealed that both patient groups produced more frequent content words than HCs [AD: t (40) = 3.682, p FDR = 0.001, d = 1.13; bvFTD: t (40) = -3.177, p FDR = 0.003, d = -0.98]. No further word property yielded significant differences in either patient group (all p FDR values > 0.36). For full details, see S3 File .

Subject-level classification

Joint analysis of all features yielded good patient identification in both cases (persons with AD vs. HCs: AUC = 0.71 ± 0.14; persons with bvFTD vs. HCs: AUC = 0.71 ± 0.14). Classification between persons with AD and HCs was mainly driven by word frequency and noun ratio, surpassing the weight of the other features by at least 50%. Conversely, classification between persons with bvFTD and HCs was similarly driven by word frequency, first-person markers, third-person markers, and noun ratio, surpassing every other feature by over 100%. ROC curves with AUC scores and feature importance rankings are shown in Fig 3 . For full details, see S4 File .


Support vector machines were used to classify between each patient group and HCs over 1000 iterations, using all linguistic features together. (A) Classification between AD patients and HCs reached AUCs of 0.71 and 0.76 in the main study and the generalizability test, respectively. These outcomes were driven by word frequency and noun ratio. (B) Classification between bvFTD patients and HCs reached AUCs of 0.71 and 0.83 in the main study and the generalizability test, respectively. These outcomes were driven by word frequency, first-person markers, third-person markers, and noun ratio. AD: Alzheimer’s dementia; AUC: Area under the receiver operating characteristic curve; bvFTD: Behavioral variant frontotemporal dementia; HCs: Healthy controls; SVM: Support vector machines.

Generalizability tests

Generalizability tests ( Fig 3 ) revealed that results remained robust, and actually improved, when training classifiers with our main study’s participants and testing them on entirely separate samples (persons with AD vs. HCs: AUC = 0.76; persons with bvFTD vs. HCs: AUC = 0.83). For full details, see S4 File .

Correlations with overall cognitive status

Correlations between connected speech features and indices of cognitive status (MoCA scores) and executive functions (IFS scores) were not significant in either patient group. See Table 2 .


We used NLP tools to examine differential markers of AD and bvFTD in patients’ expression of daily events. Relative to HCs, only persons with AD showed a reduced proportion of nouns (but not verbs), pointing to a category-specific anomaly. Conversely, only persons with bvFTD used fewer first person and more third person references than HCs, indicating more exocentric discourse. These features offered good subject-level classification in both groups, and they did not correlate with patients’ cognitive status. We elaborate on these findings below.

Word class analyses revealed differential anomalies in each patient group. Compared with HCs, persons with AD produced significantly fewer nouns and a similar proportion of verbs. The same pattern was observed in younger AD cohorts when asked open-ended self-referential questions—using both manual and automated word-class coding [ 25 , 26 ]. Thus, while AD patients may also exhibit distinct difficulties with verbs in structured tasks [ 62 ], noun retrieval may be specifically compromised in their connected speech. Importantly, this selective pattern was not observed in bvFTD patients, who, relative to HCs, produced lower proportions of both nouns and verbs. These results contrast with previous bvFTD studies reporting non-significant differences in either category relative to HCs [ 29 ] and greater difficulties with verbs than nouns compared with AD patients [ 63 ]. Yet, they all converge in the absence of differential deficits for nouns, as observed here for AD. Thus, selective reductions in noun production during spontaneous speech might afford differential markers of this dementia type.

In line with previous works [ 30 , 64 – 66 ], this selective pattern might reflect the distinct reliance of noun information on declarative memory circuits compromised by AD. Indeed, neuroimaging, neurostimulation, and lesion studies show that object naming and knowledge hinge on temporal regions [ 30 ] typically compromised in AD, such as the middle and inferior temporal lobe [ 32 ]. These regions, indeed, have been proposed to integrate information from different sensory streams, as required to adequately construe noun referents in semantic memory [ 31 ]. Although noun processing may also involve a wider distributed network spanning other regions [ 30 ], these alterations would lead AD patients to single out fewer nouns in their depiction of daily events.

Conversely, person usage results revealed a different pattern. While HCs and AD patients relied significantly more on first-person markers, bvFTD patients exhibited the opposite picture. Previous works with bvFTD patients have reported reduced insight into their own behavioral changes [ 67 ] as well as impairments in recent and remote self-related recollections, including specific and contextually rich autobiographical memories [ 37 ]. Moreover, persons with bvFTD have been shown to prefer an observer perspective when retrieving personal events, together with decreased capacity to relive sensory and/or affective details [ 39 ]. Of note, the absence of this distinction in AD aligns with previous works showing same proportions of both first- and third-person pronouns relative to HCs [ 68 ] and preserved semantic knowledge of patients’ personal history and sense of self [ 37 ]. In sum, bvFTD might be differentiated from AD by their tendency to depersonalize self-related narratives.

These results align with situated views of language disruptions, which posit that specific cognitive and socio-affective deficits are reflected in germane linguistic categories [ 69 , 70 ]. Specifically, disruption of neurocognitive systems mediating self-awareness and perspective taking would lead to reduced self-reference, leading to exocentric (third person) linguistic references to construe daily events. Indeed, self-attention [ 71 ] as well as perspective taking skills [ 72 ] have been linked to fronto-temporo-parietal hubs that are abnormally connected in bvFTD [ 73 ]. Reduced reliance on first-person markers in bvFTD, then, might be a recapitulation of more basic self-processing deficits in non-verbal domains.

Note that, in our analyses, person markers duplicated in pronoun-verb tandems were counted twice. This aimed to capture person usage in its full scope given the morpho-syntactic properties of Spanish. As a pro-drop language, Spanish allows for pronoun dropping without compromising a clause’s grammatical integrity, given that key information to establish a verb’s referent (person and number) is coded in its desinence. Yet, pronouns may well be (and are often) inserted even if the verb provides such anchorage, be it because of rhetorical (e.g., emphatic), cohesive (e.g., referential) or subjective (e.g., idiolectal) reasons. Therefore, separate counting of person markers in pronoun-verb dyads may capture relevant subject-level information, and, in any case, this premise operated equally on both first- and third-person markers, further reducing the possibility of bias in our results. More generally, this issue illustrates the importance of tackling language-specific phenomena when pursuing NLP markers of dementia, as noted in recent calls [ 42 ].

Interestingly, word property analyses did not yield syndromic differentiations. Both AD and bvFTD patients used significantly more frequent words than HCs. This differs from a previous application of the same automated pipeline, which revealed a preference for higher frequency words only in AD patients [ 41 ]. Such discrepancy might be partly explained by task demands, as Ferrante et al.’s study was based on highly controlled verbal fluency tasks. Indeed, such tasks focally target semantic memory mechanisms (which are differentially impaired in AD [ 46 ]), whereas routine description, being a spontaneous speech task [ 13 ], requires an integration of multiple context-sensitive processes that might reduce the cognitive resources available for vocabulary navigation in both syndromes—indeed, some of the non-significant features, such as semantic variability, seem useful to capture deficits in both patient groups via controlled word-level tasks [ 41 , 52 , 55 ]. Further research involving different tasks would be needed to directly test this possibility.

Additional insights come from machine learning results. Joint analysis of all features yielded good classification of both AD and bvFTD patients relative to HCs, with generalizability tests reaching AUCs of 0.76 and 0.83, respectively. Similar classification outcomes were obtained in previous machine learning studies, although these varied in the clinical grounding of their target features [ 8 , 52 , 74 ]. Of note, generalizability tests results surpassed those of our main study. This is probably because the training set of the generalizability analyses employed the entirety of the main study participants, increasing training information by 20%. This finding invites replications on even larger samples, and, more generally, reinforces the role of fine-grained, hypothesis-driven NLP metrics as markers that generalize across individual patients [ 52 ]. In deed, linguistic features were not significantly correlated with MoCA scores in either group, suggesting that they were not particularly affected in patients with higher cognitive severity. Interestingly, too, while word frequency emerged as a top discriminatory feature for both groups, this was closely followed by noun ratio in AD and by first- and third-person markers (alongside noun ratio) in bvFTD, surpassing every other feature. This reinforces the differential importance of noun processing and perspective-taking markers for each syndrome, while underscoring the value of multivariate linguistic analyses for capturing sensitive signatures of dementia [ 29 , 52 ].

While other NLP works have targeted broad collections of linguistic features in a data-driven fashion [ 8 , 45 ], our study underscores the utility of hypothesis-led assessments. The analysis of features related to each disorder’s distinct neuropsychological profile can increase interpretability and specificity, maximizing clinical utility [ 52 , 75 ]. Even with only a few variables, our study captures distinct alterations in each dementia type. In addition, our approach rests exclusively on automated tools. As such, it involves low costs and does not require highly specialized clinical staff, often limited in vulnerable world regions [ 76 ]. Briefly, then, further applications of this framework could aid the quest for scalable and equitable markers of dementia [ 41 , 42 , 53 , 77 ].

Limitations and avenues for further research

Our study is not without limitations. First, although our study was adequately powered and replicable NLP results have been obtained with similar sample sizes [ 52 , 78 ], it would be vital to test our approach with more participants. Second, our dataset lacked standard measures of person/object knowledge and perspective taking, precluding analyses of these variables relative to our target linguistic features. Future studies with such tasks would allow examining potential key drivers of the markers identified herein. Third, quantification of person markers requires specific methodological decisions guided by language-specific properties. Our earlier discussion of pronoun usage in Spanish underscores this issue and invites further questions on how such patterns might be affected by contextual factors (e.g., task demands), age (e.g., early and late-onset patients), and socioeconomic variables (e.g., education level). Future studies should expand our approach by interpolating these and other relevant variables. Fourth, while our study focused on a single task relevant to our target features (routine description), future studies should include others (e.g., story retellings, other spontaneous narratives) to establish how informative such features prove when connected speech is elicited under different processing conditions. For example, while routine description is an autobiographical memory task, story retelling may be based on non-self-referential material, and, when based on written auditory prompts, it can substantially increase memory and executive load. Future studies, then, could examine whether specific NLP markers are being optimally leveraged depending on task demands—for relevant insights, see Boschi et al. [ 13 ]. Fifth, new studies should include neuroimaging tools to reveal anatomo-functional signatures of each disorder’s linguistic alterations. Finally, cross-cultural replications would be important to ascertain whether the observed patterns generalize across different languages [ 42 ].


This work suggests that AD and bvFTD exhibit distinct alterations in their expression of daily events. While AD patients might be typified by reduced reliance on nouns, persons with bvFTD would favor exocentric (third person) perspective on events. These patterns can be captured automatically with NLP tools, which are objective, inexpensive, and scalable. Future works should further test the clinical utility of digital language markers for dementia assessments.

Supporting information

S1 file. power estimation..

S2 File. Hold-out samples’s demographics.

S1 Table . Sociodemographic profile of the hold-out samples used for generalizability tests. S2 Table . Sociodemographic profile of the entire samples, collapsing the main study and holdout samples.

S3 File. Full statistical results.

S3 Table . Word class usage: Full statistical results. S4 Table . Person usage: Full statistical results. S5 Table . Word properties: Full statistical results.

S4 File. Full machine learning results.

S6 Table . Main study: Additional performance metrics for the classification analyses. S7 Table . Generalizability tests: Additional performance metrics for the classification analyses.


We thank all participants and their families for their time and excellent predisposition. Study data were partially collected and managed using REDCap electronic data capture tools hosted at Faculty of Medicine, Universidad de Chile.

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    Part-of-speech tagging tools can automatically identify nouns and distinguish them from other categories (e.g., verbs) . Likewise, morphological tagging tools can discriminate between those coding egocentric (i.e., first-person markers, such as I and my ) and exocentric reference (e.g., third-person markers, such as she and her ) [ 44 ].

  29. Full article: An easy method to determine crucial AMEI performance

    Speech recognition in noise is even more challenging and the speech signal must be separated from the background noise or competing noise. In devices with limited MO the output of the speech signal may be limited while the noise signal in amplified to the level of the MO which diminishes the level differences between speech and noise signals.

  30. Solemnity of Most Sacred Heart of Jesus

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