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Words don’t only mean something; they also do something. In the English language, words are grouped into word classes based on their function, i.e. what they do in a phrase or sentence. In total, there are nine word classes in English.
Word class meaning and example
All words can be categorised into classes within a language based on their function and purpose.
An example of various word classes is ‘The cat ate a cupcake quickly.’
-
The = a determiner
-
cat = a noun
-
ate = a verb
-
a = determiner
-
cupcake = noun
-
quickly = an adverb
Word class function
The function of a word class, also known as a part of speech, is to classify words according to their grammatical properties and the roles they play in sentences. By assigning words to different word classes, we can understand how they should be used in context and how they relate to other words in a sentence.
Each word class has its own unique set of characteristics and rules for usage, and understanding the function of word classes is essential for effective communication in English. Knowing our word classes allows us to create clear and grammatically correct sentences that convey our intended meaning.
Word classes in English
In English, there are four main word classes; nouns, verbs, adjectives, and adverbs. These are considered lexical words, and they provide the main meaning of a phrase or sentence.
The other five word classes are; prepositions, pronouns, determiners, conjunctions, and interjections. These are considered functional words, and they provide structural and relational information in a sentence or phrase.
Don’t worry if it sounds a bit confusing right now. Read ahead and you’ll be a master of the different types of word classes in no time!
| All word classes | Definition | Examples of word classification |
| Noun | A word that represents a person, place, thing, or idea. | cat, house, plant |
| Pronoun | A word that is used in place of a noun to avoid repetition. | he, she, they, it |
| Verb | A word that expresses action, occurrence, or state of being. | run, sing, grow |
| Adjective | A word that describes or modifies a noun or pronoun. | blue, tall, happy |
| Adverb | A word that describes or modifies a verb, adjective, or other adverb. | quickly, very |
| Preposition | A word that shows the relationship between a noun or pronoun and other words in a sentence. | in, on, at |
| Conjunction | A word that connects words, phrases, or clauses. | and, or, but |
| Interjection | A word that expresses strong emotions or feelings. | wow, oh, ouch |
| Determiners | A word that clarifies information about the quantity, location, or ownership of the noun | Articles like ‘the’ and ‘an’, and quantifiers like ‘some’ and ‘all’. |
The four main word classes
In the English language, there are four main word classes: nouns, verbs, adjectives, and adverbs. Let’s look at all the word classes in detail.
Nouns
Nouns are the words we use to describe people, places, objects, feelings, concepts, etc. Usually, nouns are tangible (touchable) things, such as a table, a person, or a building.
However, we also have abstract nouns, which are things we can feel and describe but can’t necessarily see or touch, such as love, honour, or excitement. Proper nouns are the names we give to specific and official people, places, or things, such as England, Claire, or Hoover.
Cat
House
School
Britain
Harry
Book
Hatred
‘My sister went to school.‘
Verbs
Verbs are words that show action, event, feeling, or state of being. This can be a physical action or event, or it can be a feeling that is experienced.
Lexical verbs are considered one of the four main word classes, and auxiliary verbs are not. Lexical verbs are the main verb in a sentence that shows action, event, feeling, or state of being, such as walk, ran, felt, and want, whereas an auxiliary verb helps the main verb and expresses grammatical meaning, such as has, is, and do.
Run
Walk
Swim
Curse
Wish
Help
Leave
‘She wished for a sunny day.’
Adjectives
Adjectives are words used to modify nouns, usually by describing them. Adjectives describe an attribute, quality, or state of being of the noun.
Long
Short
Friendly
Broken
Loud
Embarrassed
Dull
Boring
‘The friendly woman wore a beautiful dress.’
Fig 1. Adjectives can describe the woman and the dress
Adverbs
Adverbs are words that work alongside verbs, adjectives, and other adverbs. They provide further descriptions of how, where, when, and how often something is done.
Quickly
Softly
Very
More
Too
Loudly
‘The music was too loud.’
All of the above examples are lexical word classes and carry most of the meaning in a sentence. They make up the majority of the words in the English language.
The other five word classes
The other five remaining word classes are; prepositions, pronouns, determiners, conjunctions, and interjections. These words are considered functional words and are used to explain grammatical and structural relationships between words.
For example, prepositions can be used to explain where one object is in relation to another.
Prepositions
Prepositions are used to show the relationship between words in terms of place, time, direction, and agency.
In
At
On
Towards
To
Through
Into
By
With
‘They went through the tunnel.’
Pronouns
Pronouns take the place of a noun or a noun phrase in a sentence. They often refer to a noun that has already been mentioned and are commonly used to avoid repetition.
Chloe (noun) → she (pronoun)
Chloe’s dog → her dog (possessive pronoun)
There are several different types of pronouns; let’s look at some examples of each.
- He, she, it, they — personal pronouns
- His, hers, its, theirs, mine, ours — possessive pronouns
- Himself, herself, myself, ourselves, themselves — reflexive pronouns
- This, that, those, these — demonstrative pronouns
- Anyone, somebody, everyone, anything, something — Indefinite pronouns
- Which, what, that, who, who — Relative pronouns
‘She sat on the chair which was broken.’
Determiners
Determiners work alongside nouns to clarify information about the quantity, location, or ownership of the noun. It ‘determines’ exactly what is being referred to. Much like pronouns, there are also several different types of determiners.
- The, a, an — articles
- This, that, those — you might recognise these for demonstrative pronouns are also determiners
- One, two, three etc. — cardinal numbers
- First, second, third etc. — ordinal numbers
- Some, most, all — quantifiers
- Other, another — difference words
‘The first restaurant is better than the other.’
Conjunctions
Conjunctions are words that connect other words, phrases, and clauses together within a sentence. There are three main types of conjunctions;
-
Coordinating conjunctions — these link independent clauses together.
-
Subordinating conjunctions — these link dependent clauses to independent clauses.
- Correlative conjunctions — words that work in pairs to join two parts of a sentence of equal importance.
For, and, nor, but, or, yet, so — coordinating conjunctions
After, as, because, when, while, before, if, even though — subordinating conjunctions
Either/or, neither/nor, both/and — correlative conjunctions
‘If it rains, I’m not going out.’
Interjections
Interjections are exclamatory words used to express an emotion or a reaction. They often stand alone from the rest of the sentence and are accompanied by an exclamation mark.
Oh
Oops!
Phew!
Ahh!
‘Oh, what a surprise!’
Word class: lexical classes and function classes
A helpful way to understand lexical word classes is to see them as the building blocks of sentences. If the lexical word classes are the blocks themselves, then the function word classes are the cement holding the words together and giving structure to the sentence.
Fig 2. Lexical and functional word classes
In this diagram, the lexical classes are in blue and the function classes are in yellow. We can see that the words in blue provide the key information, and the words in yellow bring this information together in a structured way.
Word class examples
Sometimes it can be tricky to know exactly which word class a word belongs to. Some words can function as more than one word class depending on how they are used in a sentence. For this reason, we must look at words in context, i.e. how a word works within the sentence. Take a look at the following examples of word classes to see the importance of word class categorisation.
The dog will bark if you open the door.
The tree bark was dark and rugged.
Here we can see that the same word (bark) has a different meaning and different word class in each sentence. In the first example, ‘bark’ is used as a verb, and in the second as a noun (an object in this case).
I left my sunglasses on the beach.
The horse stood on Sarah’s left foot.
In the first sentence, the word ‘left’ is used as a verb (an action), and in the second, it is used to modify the noun (foot). In this case, it is an adjective.
I run every day
I went for a run
In this example, ‘run’ can be a verb or a noun.
Word Class — Key takeaways
-
We group words into word classes based on the function they perform in a sentence.
-
The four main word classes are nouns, adjectives, verbs, and adverbs. These are lexical classes that give meaning to a sentence.
-
The other five word classes are prepositions, pronouns, determiners, conjunctions, and interjections. These are function classes that are used to explain grammatical and structural relationships between words.
-
It is important to look at the context of a sentence in order to work out which word class a word belongs to.
Frequently Asked Questions about Word Class
A word class is a group of words that have similar properties and play a similar role in a sentence.
Some examples of how some words can function as more than one word class include the way ‘run’ can be a verb (‘I run every day’) or a noun (‘I went for a run’). Similarly, ‘well’ can be an adverb (‘He plays the guitar well’) or an adjective (‘She’s feeling well today’).
The nine word classes are; Nouns, adjectives, verbs, adverbs, prepositions, pronouns, determiners, conjunctions, interjections.
Categorising words into word classes helps us to understand the function the word is playing within a sentence.
Parts of speech is another term for word classes.
The different groups of word classes include lexical classes that act as the building blocks of a sentence e.g. nouns. The other word classes are function classes that act as the ‘glue’ and give grammatical information in a sentence e.g. prepositions.
The word classes for all, that, and the is:
‘All’ = determiner (quantifier)
‘That’ = pronoun and/or determiner (demonstrative pronoun)
‘The’ = determiner (article)
Final Word Class Quiz
Word Class Quiz — Teste dein Wissen
Question
A word can only belong to one type of noun. True or false?
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Answer
This is false. A word can belong to multiple categories of nouns and this may change according to the context of the word.
Show question
Question
Name the two principal categories of nouns.
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Answer
The two principal types of nouns are ‘common nouns’ and ‘proper nouns’.
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Question
Which of the following is an example of a proper noun?
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Question
Name the 6 types of common nouns discussed in the text.
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Answer
Concrete nouns, abstract nouns, countable nouns, uncountable nouns, collective nouns, and compound nouns.
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Question
What is the difference between a concrete noun and an abstract noun?
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Answer
A concrete noun is a thing that physically exists. We can usually touch this thing and measure its proportions. An abstract noun, however, does not physically exist. It is a concept, idea, or feeling that only exists within the mind.
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Question
Pick out the concrete noun from the following:
Show answer
Question
Pick out the abstract noun from the following:
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Question
What is the difference between a countable and an uncountable noun? Can you think of an example for each?
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Answer
A countable noun is a thing that can be ‘counted’, i.e. it can exist in the plural. Some examples include ‘bottle’, ‘dog’ and ‘boy’. These are often concrete nouns.
An uncountable noun is something that can not be counted, so you often cannot place a number in front of it. Examples include ‘love’, ‘joy’, and ‘milk’.
Show question
Question
Pick out the collective noun from the following:
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Question
What is the collective noun for a group of sheep?
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Answer
The collective noun is a ‘flock’, as in ‘flock of sheep’.
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Question
The word ‘greenhouse’ is a compound noun. True or false?
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Answer
This is true. The word ‘greenhouse’ is a compound noun as it is made up of two separate words ‘green’ and ‘house’. These come together to form a new word.
Show question
Question
What are the adjectives in this sentence?: ‘The little boy climbed up the big, green tree’
Show answer
Answer
The adjectives are ‘little’ and ‘big’, and ‘green’ as they describe features about the nouns.
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Question
Place the adjectives in this sentence into the correct order: the wooden blue big ship sailed across the Indian vast scary ocean.
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Answer
The big, blue, wooden ship sailed across the vast, scary, Indian ocean.
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Question
What are the 3 different positions in which an adjective can be placed?
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Answer
An adjective can be placed before a noun (pre-modification), after a noun (post-modification), or following a verb as a complement.
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Question
In this sentence, does the adjective pre-modify or post-modify the noun? ‘The unicorn is angry’.
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Answer
The adjective ‘angry’ post-modifies the noun ‘unicorn’.
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Question
In this sentence, does the adjective pre-modify or post-modify the noun? ‘It is a scary unicorn’.
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Answer
The adjective ‘scary’ pre-modifies the noun ‘unicorn’.
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Question
What kind of adjectives are ‘purple’ and ‘shiny’?
Show answer
Answer
‘Purple’ and ‘Shiny’ are qualitative adjectives as they describe a quality or feature of a noun
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Question
What kind of adjectives are ‘ugly’ and ‘easy’?
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Answer
The words ‘ugly’ and ‘easy’ are evaluative adjectives as they give a subjective opinion on the noun.
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Question
Which of the following adjectives is an absolute adjective?
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Question
Which of these adjectives is a classifying adjective?
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Question
Convert the noun ‘quick’ to its comparative form.
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Answer
The comparative form of ‘quick’ is ‘quicker’.
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Question
Convert the noun ‘slow’ to its superlative form.
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Answer
The comparative form of ‘slow’ is ‘slowest’.
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Question
What is an adjective phrase?
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Answer
An adjective phrase is a group of words that is ‘built’ around the adjective (it takes centre stage in the sentence). For example, in the phrase ‘the dog is big’ the word ‘big’ is the most important information.
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Question
Give 2 examples of suffixes that are typical of adjectives.
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Answer
Suffixes typical of adjectives include -able, -ible, -ful, -y, -less, -ous, -some, -ive, -ish, -al.
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Question
What is the difference between a main verb and an auxiliary verb?
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Answer
A main verb is a verb that can stand on its own and carries most of the meaning in a verb phrase. For example, ‘run’, ‘find’. Auxiliary verbs cannot stand alone, instead, they work alongside a main verb and ‘help’ the verb to express more grammatical information e.g. tense, mood, possibility.
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Question
What is the difference between a primary auxiliary verb and a modal auxiliary verb?
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Answer
Primary auxiliary verbs consist of the various forms of ‘to have’, ‘to be’, and ‘to do’ e.g. ‘had’, ‘was’, ‘done’. They help to express a verb’s tense, voice, or mood. Modal auxiliary verbs show possibility, ability, permission, or obligation. There are 9 auxiliary verbs including ‘could’, ‘will’, might’.
Show question
Question
Which of the following are primary auxiliary verbs?
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Is
-
Play
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Have
-
Run
-
Does
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Could
Show answer
Answer
The primary auxiliary verbs in this list are ‘is’, ‘have’, and ‘does’. They are all forms of the main primary auxiliary verbs ‘to have’, ‘to be’, and ‘to do’. ‘Play’ and ‘run’ are main verbs and ‘could’ is a modal auxiliary verb.
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Question
Name 6 out of the 9 modal auxiliary verbs.
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Answer
Answers include: Could, would, should, may, might, can, will, must, shall
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Question
‘The fairies were asleep’. In this sentence, is the verb ‘were’ a linking verb or an auxiliary verb?
Show answer
Answer
The word ‘were’ is used as a linking verb as it stands alone in the sentence. It is used to link the subject (fairies) and the adjective (asleep).
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Question
What is the difference between dynamic verbs and stative verbs?
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Answer
A dynamic verb describes an action or process done by a noun or subject. They are thought of as ‘action verbs’ e.g. ‘kick’, ‘run’, ‘eat’. Stative verbs describe the state of being of a person or thing. These are states that are not necessarily physical action e.g. ‘know’, ‘love’, ‘suppose’.
Show question
Question
Which of the following are dynamic verbs and which are stative verbs?
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Drink
-
Prefer
-
Talk
-
Seem
-
Understand
-
Write
Show answer
Answer
The dynamic verbs are ‘drink’, ‘talk’, and ‘write’ as they all describe an action. The stative verbs are ‘prefer’, ‘seem’, and ‘understand’ as they all describe a state of being.
Show question
Question
What is an imperative verb?
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Answer
Imperative verbs are verbs used to give orders, give instructions, make a request or give warning. They tell someone to do something. For example, ‘clean your room!’.
Show question
Question
Inflections give information about tense, person, number, mood, or voice. True or false?
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Question
What information does the inflection ‘-ing’ give for a verb?
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Answer
The inflection ‘-ing’ is often used to show that an action or state is continuous and ongoing.
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Question
How do you know if a verb is irregular?
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Answer
An irregular verb does not take the regular inflections, instead the whole word is spelt a different way. For example, begin becomes ‘began’ or ‘begun’. We can’t add the regular past tense inflection -ed as this would become ‘beginned’ which doesn’t make sense.
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Question
Suffixes can never signal what word class a word belongs to. True or false?
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Answer
False. Suffixes can signal what word class a word belongs to. For example, ‘-ify’ is a common suffix for verbs (‘identity’, ‘simplify’)
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Question
A verb phrase is built around a noun. True or false?
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Answer
False. A verb phrase is a group of words that has a main verb along with any other auxiliary verbs that ‘help’ the main verb. For example, ‘could eat’ is a verb phrase as it contains a main verb (‘could’) and an auxiliary verb (‘could’).
Show question
Question
Which of the following are multi-word verbs?
-
Shake
-
Rely on
-
Dancing
-
Look up to
Show answer
Answer
The verbs ‘rely on’ and ‘look up to’ are multi-word verbs as they consist of a verb that has one or more prepositions or particles linked to it.
Show question
Question
What is the difference between a transition verb and an intransitive verb?
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Answer
Transitive verbs are verbs that require an object in order to make sense. For example, the word ‘bring’ requires an object that is brought (‘I bring news’). Intransitive verbs do not require an object to complete the meaning of the sentence e.g. ‘exist’ (‘I exist’).
Show question
Answer
An adverb is a word that gives more information about a verb, adjective, another adverb, or a full clause.
Show question
Question
What are the 3 ways we can use adverbs?
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Answer
We can use adverbs to modify a word (modifying adverbs), to intensify a word (intensifying adverbs), or to connect two clauses (connecting adverbs).
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Question
What are modifying adverbs?
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Answer
Modifying adverbs are words that modify verbs, adjectives, or other adverbs. They add further information about the word.
Show question
Question
‘Additionally’, ‘likewise’, and ‘consequently’ are examples of connecting adverbs. True or false?
Show answer
Answer
True! Connecting adverbs are words used to connect two independent clauses.
Show question
Question
What are intensifying adverbs?
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Answer
Intensifying adverbs are words used to strengthen the meaning of an adjective, another adverb, or a verb. In other words, they ‘intensify’ another word.
Show question
Question
Which of the following are intensifying adverbs?
-
Calmly
-
Incredibly
-
Enough
-
Greatly
Show answer
Answer
The intensifying adverbs are ‘incredibly’ and ‘greatly’. These strengthen the meaning of a word.
Show question
Question
Name the main types of adverbs
Show answer
Answer
The main adverbs are; adverbs of place, adverbs of time, adverbs of manner, adverbs of frequency, adverbs of degree, adverbs of probability, and adverbs of purpose.
Show question
Question
What are adverbs of time?
Show answer
Answer
Adverbs of time are the ‘when?’ adverbs. They answer the question ‘when is the action done?’ e.g. ‘I’ll do it tomorrow’
Show question
Question
Which of the following are adverbs of frequency?
-
Usually
-
Patiently
-
Occasionally
-
Nowhere
Show answer
Answer
The adverbs of frequency are ‘usually’ and ‘occasionally’. They are the ‘how often?’ adverbs. They answer the question ‘how often is the action done?’.
Show question
Question
What are adverbs of place?
Show answer
Answer
Adverbs of place are the ‘where?’ adverbs. They answer the question ‘where is the action done?’. For example, ‘outside’ or ‘elsewhere’.
Show question
Question
Which of the following are adverbs of manner?
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Never
-
Carelessly
-
Kindly
-
Inside
Show answer
Answer
The words ‘carelessly’ and ‘kindly’ are adverbs of manner. They are the ‘how?’ adverbs that answer the question ‘how is the action done?’.
Show question
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1. Principles of grammatical classification of words. The
traditional classification of words.
2. The syntactico-distributional classification of words.
3. The theory of three ranks (O. Jespersen).
4. The notion of lexical paradigm of nomination.
5. Functional words and their properties in the light of
— the traditional classification,
— the syntactico-distributional classification,
— the mixed approach.
6. Pronouns and their properties in the light of
— the traditional classification,
— the syntactico-distributional classification,
— the mixed approach.
1. Principles of Grammatical Classification of Words
In modern linguistic descriptions different types of word classes are
distinguished: grammatical, etymological, semantic, stylistic, etc.,
one can presume, though, that no classification can be adequate to
its aim if it ignores the grammatical principles. It is not
accidental that the theoretical study of language in the history of
science began with the attempts to identify and describe grammatical
classes of words called «parts of speech».
In Modern Linguistics parts of speech are differentiated either by a
number of criteria, or by a single criterion.
The polydifferential («traditional») classification of
words is based on the three criteria: semantic, formal, and
functional. The semantic criterion presupposes the evaluation of the
generalized (categorial) meaning of the words of the given part of
speech. The formal criterion provides for the exposition of all
formal features (specific inflectional and derivational) of all
the lexemic subsets of a particular part of speech. The functional
criterion concerns the typical syntactic functions of a part of
speech. Contractedly the set of these criteria is referred to as
«meaning, form, function».
2. Traditional Classification of Words
In accord with the traditional criteria of meaning, form, and
function, words on the upper level of classification are divided
into notional and functional.
In English to the notional parts of speech are usually referred the
noun, the adjective, the numeral, the pronoun, the verb, the adverb.
On the lines of the traditional classification the adverb, e.g., is
described in the following way: the adverb has the categorial
meaning of the secondary property (i.e. the property of process
or another property); the forms of the degrees of comparison for
qualitative adverbs, the specific derivative suffixes; the
syntactic functions of various adverbial modifiers.
The notional parts of speech are the words of complete nominative
value; in the utterance they fulfil self-dependent functions of
naming and denoting things, phenomena, their substantial
properties. Opposed to the notional parts of speech are the
functional words which are words of incomplete nominative value, but
of absolutely essential relational (grammatical) value. In the
utterance they serve as all sorts of mediators.
To the basic functional parts of speech in English are usually
referred the article, the preposition, the conjunction, the
particle, the modal word, the interjection. As has been stated
elsewhere, functional words are limited in number. On the lines
of the traditional classification they are presented by the
list, each of them requiring its own, individual description.
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Last Updated: December 18, 2021 | Author: Kurt Damiano
Contents
- 1 What is the word classifying mean?
- 2 What type of word is classification?
- 3 Why do you mean by classification?
- 4 How many types of classification are there?
What is the word classifying mean?
1 : to arrange in classes (see class entry 1 sense 3) classifying books according to subject matter. 2 : to consider (someone or something) as belonging to a particular group The movie is classified as a comedy. The vehicle is classified as a truck.
verb (used with object), clas·si·fied, clas·si·fy·ing. to arrange or organize by classes; order according to class. to assign a classification to (information, a document, etc.). Compare classification (def.
Why do you mean by classification?
Classification: Categorizing biodiversity into groups based on similarity and differences of organisms. Classification, in biology, the establishment of a hierarchical system of categories on the basis of presumed natural relationships among organisms.
How many types of classification are there?
Broadly speaking, there are four types of classification. They are: (i) Geographical classification, (ii) Chronological classification, (iii) Qualitative classification, and (iv) Quantitative classification.
Classification of Words : ETYMOLOGY
THE second part of Grammar is called ETYMOLOGY.
Etymology treats of WORDS.
CLASSIFICATION OF WORDS
The classes of words in English are nine namely NOUNS, ARTICLES, ADJECTIVES, PRONOUNS, VERBS, ADVERBS, CONJUNCTIONS, PREPOSITIONS and INTERJECTIONS.
Note : These classes of words are generally called the Parts of Speech.
NOUNS
A Noun is the name of any person, place or thing as, boy, school, and book.
What is your name?
Give the names of five personas that you know.
What is the name of the place in which you live?
Give the names of five other places.
Name five things that you can see.
Name five things that you can think of but cannot see.
All these names are nouns.
In the sentence, “Charles wont to Boston in the boat,» what part of speech is Charles? Why? Boston? Why? Boat? Why?
In the following sentences state which words are nouns and why?
In coming from Trenton to Philadelphia, I saw John on the boat with a satchel of books in his hands.
The book had good covers, but bad print
The boy had a knife with a small blade.
The horse in the stable has a good disposition.
Temperance and industry promote health.
Religion exalts a nation.
Beauty is a fading flower.
Note : The teacher should repeat the foregoing exercises and form others like them, until the learner becomes familiar with the subject and can go through any sentence and indicate the nouns with facility.
Name (or write) five nouns.
CLASSIFICATION OF NOUNS
Nouns are divided into two general classes — PROPER NOUNS and COMMON NOUNS.
A Proper noun is a name given to only one of a class of objects as John, London, Delaware.
Note : A Proper noun should always begin with a capital letter.
A Common noun is a name given to any one of a class of objects as boy, city, and river.
Explanation : There is a class of objects called BOYS. The name BOY is given to any one of that class. It is common to them all.
One particular boy is called JOHN. That is his particular name. It is peculiar or proper to him. So CITY is a name given in common to any one of another class of objects. But LONDON is the name given to one particular city. It belongs peculiarly and properly to that city. Any one of a certain other class of objects is called a RIVER. The name is common to all such objects. But one particular object of this kind is called DELAWARE. It belongs properly to that particular river.
A Collective noun is the name of a collection of objects considered as one as army, crowd.
Exercises :
Which of the following nouns are Proper Nouns?
Which of the following nouns are Common Nouns?
Which of the following nouns are Collective Nouns?
James, Isaiah, prophet, Australia, island, regiment, Plymouth, town, herd,
Washington, England, county, flock, Elizabeth, woman, class, table, chair, book, Hudson.
Which of the foregoing nouns should begin with a capital letter?
What is your own proper name?
What is your common name?
Name (or write) six proper nouns, six common nouns, six collective nouns.
ATTRIBUTES OF NOUNS
Nouns have the attributes of GENDER, NUMBER, PERSON and CASE.
GENDER
Gender is the distinction of nouns in regard to SEX.
Nouns have three genders, MASCULINE, FEMININE and NEUTER.
The Masculine denotes objects of THE MALE SEX as boy, man.
The Feminine denotes objects of THE FEMALE SEX as girl, woman.
The Neuter denotes objects WITHOUT SEX as book, river.
Classification of Words :
Classification of Words To HOME PAGE Elementary English Grammar Index
Text classification is the process of analyzing text sequences and assigning them a label, putting them in a group based on their content. Text classification underlies almost any AI or machine learning task involving Natural Language Processing (NLP). With text classification, a computer program can carry out a wide variety of different tasks like spam recognition, sentiment analysis, and chatbot functions. How does text classification work exactly? What are the different methods of carrying out text classification? We’ll explore the answers to these questions below.
Defining Text Classification
It’s important to take some time and make sure that we understand what text classification is, in general, before delving into the different methods of doing text classification. Text classification is one of those terms that is applied to many different tasks and algorithms, so it’s useful to make sure that we understand the basic concept of text classification before moving on to explore the different ways that it can be carried out.
Anything that involves creating different categories for text, and then labeling different text samples as these categories, can be considered text classification. As long as a system carries out these basic steps it can be considered a text classifier, regardless of the exact method used to classify the text and regardless of how the text classifier is eventually applied. Detecting email spam, organizing documents by topic or title, and recognizing the sentiment of a review for a product are all examples of text classification because they are accomplished by taking text as an input and outputting a class label for that piece of text.
How Does Text Classification Work?
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Most text classification methods can be placed into one of three different categories: rule-based methods or machine learning methods.
Rule-Based Classification Methods
Rule-based text classification methods operate through the use of explicitly engineered linguistic rules. The system uses the rules created by the engineer to determine which class a given piece of text should belong to, looking for clues in the form of semantically relevant text elements. Every rule has a pattern that the text must match to be placed into the corresponding category.
To be more concrete, let’s say you wanted to design a text classifier capable of distinguishing common topics of conversation, like the weather, movies, or food. In order to enable your text classifier to recognize discussion of the weather, you might tell it to look for weather-related words in the body of the text samples it is being fed. You’d have a list of keywords, phrases, and other relevant patterns that could be used to distinguish the topic. For instance, you might instruct the classifier to look for words like “wind”, “rain”, “sun”, “snow”, or “cloud”. You could then have the classifier look through input text and count the number of times that these words appear in the body of the text and if they appear more commonly than words related to movies, you would classify the text as belonging to the weather class.
The advantage of rules-based systems is that their inputs and outputs are predictable and interpretable by humans, and they can be improved through manual intervention by the engineer. However, rules-based classification methods are also somewhat brittle, and they often have a difficult time generalizing because they can only adhere to the predefined patterns that have been programmed in. As an example, the word “cloud” could refer to moisture in the sky, or it could be referring to a digital cloud where data is stored. It’s difficult for rules-based systems to handle these nuances without the engineers spending a fair amount of time trying to manually anticipate and adjust for these subtleties.
Machine Learning Systems
As mentioned above, rules-based systems have limitations, as their functions and rules must be pre-programmed. By contrast, machine learning-based classification systems operate by applying algorithms that analyze datasets for patterns that are associated with a particular class.
Machine learning algorithms are fed pre-labeled/pre-classified instances that are analyzed for relevant features. These pre-labeled instances are the training data.
The machine learning classifier analyzes the training data and learns patterns that are associated with the different classes. After this, unseen instances are stripped of their labels and fed to the classification algorithm which assigns the instances a label. The assigned labels are then compared to the original labels to see how accurate the machine learning classifier was, gauging how well the model learned what patterns predict which classes.
Machine learning algorithms operate by analyzing numerical data. This means that in order to use a machine learning algorithm on text data, the text needs to be converted into a numerical format. There are various methods of encoding text data as numerical data and creating machine learning methods around this data. We’ll cover some of the different ways to represent text data below.
Bag-of-Words
Bag-of-words is one of the most commonly used approaches for encoding and representing text data. The term “bag-of-words” comes from the fact that you essentially take all the words in the documents and put them all into one “bag” without paying attention to word order or grammar, paying attention only to the frequency of words in the bag. This results in a long array, or vector, containing a single representation of all the words in the input documents. So if there are 10000 unique words total in the input documents, the feature vectors will be 10000 words long. This is how the size of the word bag/feature vector is calculated.
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After the feature vector size has been determined, every document in the list of total documents is assigned its own vector filled with numbers that indicate how many times the word in question appears in the current document. This means that if the word “food” appears eight times within one text document, that corresponding feature vector/feature array will have an eight in the corresponding position.
Put another way, all the unique words that appear in the input documents are all piled into one bag and then each document gets a word vector of the same size, which is then filled in with the number of times the different words appear in the document.
Text datasets will often contain a large number of unique words, but most of them aren’t used very frequently. For this reason, the number of words used to create the word vector is typically capped at a chosen value (N) and then the feature vector dimension will be Nx1.
Term Frequency-Inverse Document Frequency (TF-IDF)
Another way to represent a document based on the words in it is dubbed Term Frequency-Inverse Document Frequency (TF-IDF). A TF-IDF approach also creates a vector that represents the document based on the words in it, but unlike Bag-of-words these words are weighted by more than just their frequency. TF-IDF considers the importance of the words in the documents, attempting to quantify how relevant that word is to the subject of the document. In other words, TF-IDF analyzes relevance instead of frequency and the word counts in a feature vector are replaced by a TF-IDF score that is calculated with regard to the whole dataset.
A TF-IDF approach operates by first calculating the term frequency, the number of times that the unique terms appear within a specific document. However, TF-IDF also takes care to limit the influence that extremely common words like “the”, “or”, and “and”, as these “stopwords” are very common yet convey very little information about the content of the document. These words need to be discounted, which is what the “inverse-document frequency” part of TF-IDF refers to. This is done is because the more documents that a specific words shows up in, the less useful that word is in distinguishing it from the other documents in the list of all documents. The formula that TF-IDF uses to calculate the importance of a word is designed to preserve the words that are the most frequent and the most semantically rich.
The feature vectors created by the TF-IDF approach contain normalized values that sum to one, assigning each word a weighted value as calculated by the TF-IDF formula.
Word Embeddings
Word embeddings are methods of representing text that ensure that words with similar meanings have similar numerical representations.
Word embeddings operate by “vectorizing” words, meaning that they represent words as real-valued-vectors in a vector space. The vectors exist in a grid or matrix, and they have a direction and length (or magnitude). When representing words as vectors, the words are converted into vectors comprised of real values. Every word is mapped to one vector, and words that are similar in meaning have similar direction and magnitude. This type of encoding makes it possible for a machine learning algorithm to learn complicated relationships between words.
The embeddings that represent different words are created with regard to how the words in question are used. Because words that are used in similar ways will have similar vectors, the process of creating word embeddings automatically translates some of the meaning the words have. A bag of words approach, by contrast, creates brittle representations where different words will have dissimilar representations even if they are used in highly similar contexts.
As a result, word embeddings are better at capturing the context of words within a sentence.
There are different algorithms and approaches used to create word embeddings. Some of the most common and reliable word embedding methods include: embedding layers, word2vec, and GloVe.
Embedding Layers
One potential way to use word embeddings alongside a machine learning/deep learning system is to use an embedding layer. Embedding layers are deep learning layers that convert words into embeddings which is then fed into the rest of the deep learning system. The word embeddings are learned as the network trains for a specific text-based task.
In a word embedding approach, similar words will have similar representations and be closer to each other than to dissimilar words.
In order to use embedding layers, the text needs to be preprocessed first. The text in the document has to be one-hot encoded, and the vector size needs to be specified in advance. The one-hot text is then converted to word vectors and the vectors are passed into the machine learning model.
Word2Vec
Word2Vec is another common method of embedding words. Word2Vec uses statistical methods to convert words to embeddings and it is optimized for use with neural network based models. Word2Vec was developed by Google researchers and it is one of the most commonly used embedding methods, as it reliably yields useful, rich embeddings. Word2Vec representations are useful for identifying semantic and syntactic commonalities in language. This means that Word2Vec representations capture relationships between similar concepts, being able to distinguish that the commonality between “King” and “Queen” is royalty and that “King” implies “man-ness” while Queen implies “Woman-ness”.
GloVe
GloVE, or Global Vector for Word Representation, builds upon the embedding algorithms used by Word2Vec. GloVe embedding methods combine aspects of both Word2Vec and matrix factorization techniques like Latent Semantic Analysis. The advantage of Word2Vec is that it can capture context, but as a tradeoff it poorly captures global text statistics. Conversely, traditional vector representations are good at determining global text statistics but they aren’t useful for determining the context of words and phrases. GloVE draws from the best of both approaches, creating word-context based on global text statistics.