Finding word synonyms and antonyms - Word и Excel - помощь в работе с программами

Методическая
разработка по английскому языку для учащихся 10-11 классов

Part2. ЛЕКСИКА В
УПРАЖНЕНИЯХ

Синонимы, антонимы

(Synonyms and Antonyms)

Основная цель данной разработки
состоит в том, чтобы помочь учащимся в кратчайшие
сроки активизировать ранее изученный лексико- грамматический материал и под
готовиться к успешной сдаче ЕГЭ по английскому языку.

Данные задания направлены на проверку
знаний основных проблемных областей грамматики и лексики,
без которых невозможна успешная сдача ЕГЭ. Упражнения снабжены краткими справочными материалами. После каждого
раздела вы найдёте ключи к заданиям.

A synonym is a word with the same meaning as
another word in the same lan
guage. An antonym is a word that means the opposite of another word.

Example: quickly – fast
(Synonyms) tall – short (Antonyms)

TASK 1

Match the words in the left column with their synonyms in the right column.

1.
glad A. odd

2.
quickly B. unexpectedly

3.
to understand C. pleased

4.
strange D. to
vanish

5.
to jump E. slender

6.
suddenly F. irritated

7.
slim G. fast

8.
to disappear H. likely

9.
angry I. to hop

10.
probably J. to realize

TASK 2

Fill in the table. Write as many synonyms as you can of the following words.

beautiful	clever	ugly	silly
good looking	intelligent	unattractive	stupid

beautiful

clever

ugly

silly

TASK 3

A. Complete
the sentences with the synonyms of the word ‘dangerous’.

1.
The road was wet and it was very ri to drive fast.

2.
The journey is going to be extremely ha .

3.
I’m afraid this water
is un to drink.

4.
Tr travel conditions were predicted for much of Britain today.

5.
We found ourselves in a pe situation.

B. Complete
the sentences with the synonyms of the word ‘hurry’.

1.
You don’t have to ru . The meeting is at 5 pm.

2.
If you don’t hu , you will miss the train.

3.
Suddenly she said that she had to go and ha home.

4.
The children ra downstairs to meet their parents.

5.
When I saw him I da across the road.

TASK 4

Match the words in the left column with their antonyms in the right column.

1.
to love A. to
leave

2.
friend B. to
lose

3.
natural C. rude

4.
joy D. enemy

5.
to arrive E. relaxed

6.
success F. to hate

7.
to find G. thin

8.
polite H. artificial

9.
ill at ease I. failure

10.
fat J. sorrow

TASK 5

Fill in the table. Write as many antonyms as you can of the following words.

happy	honest	easy	kind
unhappy	dishonest	difficult	unkind

TASK 6

Decide which of the three following words means the opposite.

1.
antagonistic

a.
hostile b. friendly c. realistic

2.
industrious

a.
hard working b. productive c. idle

3.
straightforward

a.
simple b. complicated c. confident

4.
malicious

a.
kind b. cruel c. suspicious

5.
embarrassed

a.
nervous b. ashamed c. comfortable

6.
overlook

a.
notice b. miss c. avoid

7.
despise

a.
loathe b. respect c. scare

TASK 7

Complete the sentences
with the opposites
of the words in brackets.

1.
I didn’t like his (sincere) smile.

2.
The boy was punished
for his (obedience)

3.
I saw that Tom was (patient) to leave.

4.
Mr. Atkinson is a very nice person. You must have (understood) him.

5.
‘Bike’ is an (formal)
word for ‘bicycle’.

6.
The child was absolutely (controllable)

7.
Nick is Mr. Austin’s
(legitimate) son.

8.
I strongly (agree) with your statement!

beautiful

clever

ugly

silly

good looking pretty

attractive
lovely

handsome
cute

gorgeous
stunning

magnificent picturesque breathtaking exquisite

elegant

intelligent smart

brilliant bright wise

gifted brainy

cunning
crafty

unattractive hideous

unpleasant unsightly

repulsive
grotesque

stupid foolish unwise daft

dumb

happy

honest

easy

kind

unhappy
sad

gloomy
wistful

mournful
glum

down

downcast
dejected

dishonest
sly

devious corrupt sneaky

unscrupulous
fraudulent

underhand

difficult
hard

tough

challenging demanding tricky

awkward daunting delicate

unkind
cruel wicked nasty

horrible
malicious mean

hard hearted hurtful

unsympathetic
spiteful

KEYS

TASK 1

1.
C

2.
G

3.
J

4.
A

5.
I

6.
B

7.
E

8.
D

9.
F

10.
H

TASK
5

TASK 3

1.
risky

2.
hazardous

3.
unsafe

4.
treacherous

5.
perilous

1.
rush

2.
hustle

3.
hastened

4.
raced

5.
dashed

TASK 4

1.
F

2.
D

3.
H

4.
J

5.
A

6.
I

7.
B

8.
C

9.
E

10.
G

TASK
5

TASK
6

1.
b

2.
c

3.
b

4.
a

5.
c

6.
a

7.
b

TASK 7

1.
insincere

2.
disobedience

3.
impatient

4.
misunderstood

5.
informal

6.
uncontrollable

7.
illegitimate

8.
disagree

Источник

Synonyms and Antonyms! In this lesson, you will learn synonyms and antonyms of common words in English.

Synonyms of Words

Adult >>> grown person

After >>> following, succeeding

Aggressive >>> hostile

Always >>> all the time

Apparent >>> evident, obvious

Arrive >>> come

Artificial >>> false, insincere

Authentic >>> genuine

Awkward >>> unpleasant

Before >>> prior to, previous to

Believe >>> trust

Beneficial >>> advantageous

Brave >>> courageous

Brief >>> short

Busy >>> occupy

Calm >>> still

Care >>> bother

Cease >>> stop, finish

Charming >>> delightful, pleasant

Chubby >>> plump

Close >>> end

Combine >>> integrate

Complex >>>compound

Comprehend >>> understand

Condemn >>> criticize

Confess >>> admit

Conflict >>> dispute

Conform >>> obey, follow

Congested >>> crowed, full

Conscientious >>> diligent

Consecutive >>> successive

Considerate >>> thoughtful

Contaminate >>> pollute

Continue >>> go on

Convenient >>> suitable, appropriate

Correct >>> right

Courteous >>> polite, well behaved

Cozy >>> warm, comfortable

Crazy >>> mad

Cry >>> weep

Dangerous >>> threatening

Dark >>> unlighted, black

Dead>>> passed away

Deduct >>> subtract

Defy >>> escape, defeat

Demolish >>> destroy

Dense >>> thick, heavy

Deposit >>> prepayment

Despise >>> detest, hate

Destroy >>>demolish

Deter >>> discourage

Die >>> pass away

Difficult >>> hard

Diminish >>> decrease

Disagree >>> contradict

Diverse >>> various

Docile >>> obedient

Doubt >>> uncertainty

Drastic >>> serious

Dry >>> withered

Dull >>> drab, dreary, somber

Early >>> advance

Eccentric >>> abnormal

Encourage >>> cheer, motivate

Enjoy >>> like

Enormous >>> huge, vast

Entirely >>> totally, fully

Evident >>> apparent, obvious

Exceptional >>> abnormal, unusual

Exhilarated >>> thrill

Exquisite >>> beautiful, lovely, elegant

Fast >>> quick

Feasible >>> possible

Few >>> any

Fill >>> enough, sufficient, plenty

Follow >>> accompany

Former >>> prior

Frank >>> straight

Frigid >>> frozen, freezing

Full >>> filled

Gallant >>> gentlemanly

Gaunt >>> skinny

Gentle >>> kind

Give >>> present

Gloomy >>> dark, shadowy

Good >>> wonderful, great

Gratitude >>> gratefulness

Hard >>> difficult

Help >>> assist, aid

Hold >>> carry, keep

Hospitable >>> friendly

Huge >>> big, vast

Humiliate >>> embarrass, mortify, humble

Idle >>> lazy

Immature >>> childish

Impartial >>> neutral

Imperative >>> vital, crucial

Independent >>> individualistic

Insane >>> mad

Intelligent >>> clever

Internal >>> interior

Irrelevant >>> immaterial

Join >>> connect

Keep >>> remain, stay

Large >>> big

Least >>> smallest

Long >>> prolonged

Magnify >>> enlarge

Maximum >>> highest

Mean >>> intend, aim

Migrant >>> immigrant

Minor >>> slight, small

Negligent >>> careless, irresponsible

Nice >>>pleasant

Numerous >>> many

Oblivious >>> unaware, unconscious

Observe >>> notice

Offend >>> upset

Optimistic >>> positive

Ordinary >>> usual, normal

Outstanding >>> excellent

Passive >>> submissive

Patience >>> tolerance

Perfect >>> ideal

Poor >>> poverty-stricken

Possible >>> feasible

Precious >>> valuable

Premature >>> early

Preserve >>> conserve

Prevalent >>> common

Probable >>> likely

Profit >>> gain

Prominent >>> well known

Prosperous >>> flourishing, successful,

Push >>> shove, thrust

Question >>> inquiry, query

Quit >>> leave, stop

Radiant >>> bright

Relevant >>> pertinent

Reluctant >>> unwilling

Resist >>> withstand

Ridiculous >>> hilarious

Sad >>> unhappy, sorrowful

Scarce >>> short, insufficient

Seize >>> grab

Sick >>> ill, unwell

Smooth >>> even

Sorrow >>> sadness, unhappiness

Spontaneous >>> unplanned

Stationary >>> motionless

Stop >>> end, finish

Stupid >>> unintelligent, brainless

Successful >>> victorious

Superb >>> excellent, superlative

Surplus >>> excess

Tender >>> caring, kind

Trivial>>> unimportant, insignificant

Upset >>> distress

Vacant >>> empty, unoccupied, unfilled

Valiant >>> brave

Vicious >>> violent

Wealth >>> affluence, prosperity

Wholehearted >>> sincere

Win >>> gain

Wonderful >>> marvelous, magnificent, superb,

Wrong >>> incorrect, mistaken

Antonyms of Words

Adult >>> child

After >>> before

Aggressive >>> passive

Always >>> never

Apparent >>> hidden

Arrive >>> depart

Artificial >>> real, authentic

Authentic >>> false, artificial

Awkward >>> graceful

Before >>> after, behind

Believe >>> doubt, distrust

Beneficial >>> harmful, adverse

Brave >>> cowardly, timid

Brief >>> long

Busy >>> idle, inactive

Calm >>> excited

Care >>> neglect

Cease >>> continue

Charming >>> obnoxious

Chubby >>> thin, skinny

Close >>> open

Combine >>> separate

Complex >>> simple

Comprehend >>> confuse, misinterpret

Condemn >>> approve

Confess >>> deny

Conflict >>> agree

Conform >>> dissent, dispute

Congested >>> empty

Conscientious >>> neglectful

Consecutive >>> interrupted

Considerate >>> thoughtless

Contaminate >>> purify

Continue >>> discontinue, stop

Convenient >>> inconvenient

Correct >>> wrong, incorrect

Courteous >>> rude

Cozy >>> uncomfortable

Crazy >>> sane

Cry >>> laugh

Dangerous >>>safe

Dark >>> light

Dead >>> alive

Deduct >>> add

Defy >>> obey, comply

Demolish >>> restore

Dense >>> sparse

Deposit >>> withdraw

Despise >>> love

Destroy >>>restore

Deter >>> encourage

Die >>> live

Difficult >>> easy

Diminish >>> increase

Disagree >>> agree

Diverse >>> same, similar

Docile >>> wild

Doubt >>> believe

Drastic >>> mild, moderate

Dry >>> wet

Dull >>> sharp, bright

Early >>> late

Eccentric >>> normal

Encourage >>> discourage

Enjoy >>> dislike

Enormous >>> tiny

Entirely >>> partly

Evident >>> doubtful, vague

Exceptional >>> ordinary, usual

Exhilarated >>> depressed

Exquisite >>> revolting

Fast >>> slow

Feasible >>> impossible

Few >>> many

Fill >>> empty

Follow >>> precede

Former >>> latter

Frank >>> evasive

Frigid >>> warm, hot

Full >>> empty

Gallant >>> ungentlemanly

Gaunt >>> overweight, plump

Gentle >>> rough, harsh

Give >>> take, receive

Gloomy >>> cheery, bright

Good >>> bad, awful

Gratitude >>> ungratefulness

Hard >>> soft, easy

Help >>> hinder, thwart

Hold >>> release, discharge

Hospitable >>> rude, unfriendly

Huge >>> small, tiny

Humiliate >>> honor, dignify

Idle >>> busy

Immature >>> mature, adult

Impartial >>> prejudiced

Imperative >>> unnecessary

Independent >>> dependent, unsure

Insane >>> sane

Intelligent >>> ignorant, dense

Internal >>> external, outer

Irrelevant >>> relevant

Join >>> separate

Keep >>> discard

Large >>> small

Least >>> most

Long >>>short

Magnify >>> reduce, minimize

Maximum >>> minimum, least

Mean >>> pleasant, nice

Migrant >>> stationary, immovable

Minor >>> major

Negligent >>> conscientious

Nice >>> unpleasant

Numerous >>> few, scanty

Oblivious >>> mindful, aware

Observe >>> ignore, disregard

Offend >>> please, delight

Optimistic >>> pessimistic

Ordinary >>> unusual

Outstanding >>> insignificant

Passive >>> forceful

Patience >>> impatience

Perfect >>> imperfect, faulty

Poor >>> rich, wealthy

Possible >>> impossible

Precious >>> cheap, worthless

Premature >>> late, delayed

Preserve >>> destroy, neglect

Prevalent >>> uncommon

Probable >>> improbable, doubtful

Profit >>> loss

Prominent >>> unknown

Prosperous >>> unsuccessful, fruitless

Push >>> pull

Question >>> answer

Quit >>> continue, remain

Radiant >>> dim

Relevant >>> irrelevant, insignificant

Reluctant >>> willing

Resist >>> comply, conform

Ridiculous >>> sensible, believable

Sad >>> happy, glad

Scarce >>> plentiful, abundant

Seize >>> release, free

Sick >>> well, healthy

Smooth >>> rough

Sorrow >>> joy, ecstasy

Spontaneous >>> planned, rehearsed

Stationary >>> movable, portable

Stop >>> start, begin

Stupid >>> knowledgeable, smart

Successful >>> failing, unsuccessful

Superb >>> inferior, mediocre

Surplus >>> lack, deficit

Tender >>> harsh, rough

Trivial >>> important, crucial

Upset >>> soothe, calm

Vacant >>> filled, occupied

Valiant >>> cowardly, fearful

Vicious >>> kind

Wealth >>> poverty

Wholehearted >>> insincere

Win >>> lose

Wonderful >>> awful

Wrong >>> correct, right

Antonyms and Synonyms| Infographic

Synonyms and Antonyms |Infographic 1

Synonyms and Antonyms |Infographic 2

Antonyms and Synonyms|Infographic 3

Antonyms and Synonyms|Infographic 4

Источник

November 11, 2021
Vocabulary

Learn Some common but important synonyms in english. Words with Synonyms and Antonyms in English with ESL infographics. Synonyms are the words with similar meaning and antonyms are words with opposite meaning. Synonyms and antonyms will help in english speaking. To improve the your english skills you must have to learn these synonyms and antonyms in english language.

Today we are going to learn some useful synonyms and antonyms in english. This list of 1000+ Synonyms along with their antonyms is very important and that why you must have to learn them all. We use these synonyms in our daily life and that why they are important. We have created this list with the help our ESL expert and you can’t ignore the important of these synonyms and antonyms.

Antonyms are the words with opposite meaning. Another name for antonyms in of opposite words. You can use opposite words in place of antonyms too. Learn antonyms will add new words to your vocabulary and you can talk about any thing without any problem.

Synonyms are the very important part of english grammar and vocabulary and that’s why we are learning synonyms. Synonyms are the similar words, they are the words with exactly same meaning. Below is the list of 1000+ Synonyms along with their antonyms.

Synonyms and Antonyms in English

Words	Synonyms	Antonyms
Compress	abbreviate, shrink	amplify, expand
Obtain	access, inherit	forfeit
Busy	active, engaged	idle, lazy
Accord	agreement, harmony	discord
Tenement	apartment, digs	breakeven, dislodge
Sterile	barren, impotent	profitable, potent
Winsome	beautiful, comely	alluring, rapturous
Remonstrate	censure, protest	agree, loud
Enormous	colossal, mammoth	diminutive, negligible
Capable	competent, able	incompetent, inept
Ornamental	decorative, adorned	unseemly, plain
Abject	despicable, servile	commendable, praiseworthy
Impair	diminish, deteriorate	restore, revive
Contrary	dissimilar, conflicting	similar, alike
Vivid	eloquent, lucid	dull, dim
Bawdy	erotic, coarse	decent, moral
Impulsive	flaky, impetuous	cautious, deliberate
Pertness	flippancy, impudence	modesty, diffidence
Mandatory	imperative, requisite	optional
Zig -zag	oblique, wayward	straight, unbent
Gloom	obscurity, darkness	delight, mirth
Superficial	partial, shallow	profound, discerning
Taboo	prohibit, ban	permit, consent
Rapidity	quickness, velocity	inertia, lanquidity
Keen	sharp, poignant	vapid, insipid
Consolidate	solidify, strengthen	separate, weaken
Defray	spend, pay	disclaim, repudiate
Merit	stature, asset	demerit, dishonor
Hapless	unfortunate, ill-fated	fortunate, lucky
Immaculate	unsullied, spotless	defiled, tarnished
Feeble	weak, frail	strong, robust
Bewitching	alluring, charming	repulsive, repugnant
Candid	blunt, bluff	evasive
Calculating	canny, devious	artless, honest
Consolidate	centralize, fortify	weaken
Umbrage	chagrin, offense	sympathy, goodwill
Lenient	compassionate, merciful	cruel, severe
Knotty	complicated difficult	simple, manageable
Vanity	conceit, pretension	modesty, humility
Yoke	connect, harness	liberate, release
Infernal	damned, accursed	heavenly,
Hypocrisy	deception, pharisaism	sincerity, frankness
Subvert	demolish, sabotage	generate, organize
Timid	diffident, coward	bold, intrepid
Resentment	displeasure, wrath	content, cheer
Consequence	effect, outcome	origin, start
Solicit	entreat, implore	protest oppose
Indifferent	equitable, haughty	partial, biased
Obvious	evident, apparent	obscure, ambiguous
Pompous	haughty, arrogant	unpretentious, humble
Adjunct	joined, added	separated, subtracted
Eager	keen, acquisitive	indifferent, apathetic
Sublime	magnificent, eminent	ridiculous
Momentous	notable, eventful	trivial, insignificant
Allay	pacify, soothe	aggravate, excite
Docile	pliable, pliant	headstrong, obstinate
Nimble	prompt, brisk	sluggish, languid
Quaint	queer, strange	familiar, usual
Glut	stuff, satiate	starve, abstain
Insipid	tedious, prosaic	pleasing, appetizing
Frantic	violent, agitated	subdued, gentle
Hasty	abrupt, impetuous	leisurely, cautious
Axiom	adage, truism	absurdity, blunder
Obscure	arcane, vague	prominent
Valor	bravery, prowess	fear, cowardice
Brittle	breakable, crisp	tough, enduring
Condemn	castigate, chide	approve, praise
Tame	compliant, subdued	wild, untamed
Admonish	counsel, reprove	approve, applaud
Ecstasy	delight, exultation	despair, calamity
Murky	dusky, dreary	bright, shining
Catholic	generic, liberal	narrow- minded
Sarcastic	ironical, derisive	courteous, gracious
Gorgeous	magnificent, dazzling	dull, unpretentious
Wed	marry, combine	divorce, separate
Ordain	order, impose	revoke abolish
Luscious	palatable, delicious	unsavory, tart
Peevish	perverse, sullen	suave, amiable
Censure	rebuke, reprimand	praise, acceptance
Retract	recant, withdraw	confirm, assert
Dissuade	remonstrate, counsel	insite, persuade
Rustic	rural, uncivilized	cultured, refined
Derogatory	sarcastic, critical	laudatory, appreciative
System	scheme, entity	chaos, disorder
Tenacious	stubborn, dodge	docile, non- resinous
Falter	stumble, demur	persist, endure
Zenith	summit, apex	nadir, base
Judicious	thoughtful, prudent	irrational, foolish
Invincible	unconquerable, impregnable	effeminate, languid
Ingenuous	undisguised, naive	wily, craftly
Malice	vengefulness, grudge	goodwill, kindness
Base	vulgar, coarse	summit, noble

Important Synonyms and Antonyms

Words	Synonyms	Antonyms
Invective	accusation, censure	approval, acclamation
Consent	agree, permit	object disagree
Generosity	altruism, bounty	stinginess, greed
Tranquil	amicable, calm	agitated, fierce
Rescind	annul, abrogate	delegate, permit
Audacity	boldness, courage	mildness, cowardice
Destructive	catastrophic, pernicious	creative, constructive
Reluctant	cautious, averse	anxious, eager
Boisterous	clamorous, rowdy	placid, calm
Hamstrung	cripple debilitate	strengthen, encourage
Lunacy	delusion, insanity	normalcy, sanity
Contradict	deny, oppose	approve, confirm
Forsake	desert, renounce	hold, maintain
Barren	desolate, sterile	damp, fertile
Efface	destroy, obliterate	retain, maintain
Disdain	detest, despise	approve, praise
Outbreak	eruption, insurrection	compliance, subjection
Virtue	ethic, morality	vice, dishonesty
Adherent	follower, disciple	rival, adversary
Paramount	foremost, eminent	trivial, inferior
Thrifty	frugal, prudent	extravagant
Courtesy	generosity, reverence	disdain, rudeness
Molest	harass, tease	console, soothe
Just	honest, impartial	unequal, unfair
Propagate	inseminate, fecundate	suppress, deplete
Stupor	lethargy, unconsciousness	sensibility, consciousness
Waive	relinquish, remove	impose, clamp
Servile	slavish, docile	aggressive, dominant
Intricate	tangled, complicated	regulated, orderly
Novice	tyro, beginner	veteran, ingenious
Offensive	abhorrent, obnoxious	engaging, fascinating
Genuine	absolute, factual	spurious
Concord	agreement, accord	discord
Baffle	astound, faze	facilitate, clarify
Bleak	austere, blank	bright, cheerful
Captivate	charm, fascinate	disillusion offend
Utterly	completely, entirely	deficiently, incomplete
Notion	conceit, apprehension	reality, concrete
Shrewd	cunning, craftly	simple, imbecile
Slander	defame, malign	applaud, approve
Eclipse	diminution, dimming	shine, eclipse
Jejune	dull, boring	interesting, exciting
Interesting	enchanting, riveting	dull, uninteresting
Quibble	equivocate, prevaricate	unfeigned, plain
Immaculate	exquisite, impeccable	defiled, tarnished
Incongruous	inappropriate, absurd	compatible, harmonious
Comprise	include, contain	reject, lack
Raid	incursion, foray	retreat, release
Harass	irritate, molest	assist, comfort
Liberal	magnanimous, generous	stingy, malicious
Deride	mock, taunt	inspire, encourage
Predicament	plight, dilemma	resolution, confidence
Mutinous	recalcitrant, insurgent	submissive, faithful
Scanty	scarce, insufficient	lavish, multitude
Feud	strife, quarrel	harmony, fraternity
Adamant	stubborn, inflexible	flexible, soft
Wile	trickery, artifice	naivety, honor
Abortive	vain, unproductive	productive
Tremble	vibrate	steady
Vagrant	wander, roaming	steady, settled
Authentic	accurate, credible	fictitious, unreal
Cunning	acute, smart	naive, coarse
Amplify	augment, deepen	lessen, contract
Befogged	becloud, dim	clear headed, uncloud
Compact	bunched, thick	loose, diffuse
Harmony	conformity, amicability	discord, discord
Ratify	consent, approve	deny, dissent
Ferocious	cruel, fierce	gentle, sympathetic
Oracular	cryptic, vague	lucid, distinct
Grisly	disgusting, atrocious	pleasing, attractive
Allure	entice, fascinate	repulse, repel
Wield	exert, employ	forgo, avoid
Perturbed	flustered, anxious	calm
Masculine	gallant, strapping	feminine, meek
Obstruct	impede, prevent	hasten, encourage
Instill	inculcate, inject	eradicate, extract
Sneer	mock, scorn	flatter, praise
Dense	opaque, piled	sparse, brainy
Tranquil	peaceful, composed	violent, furious
Epitome	precise, example	increment, expansion
Successful	propitious, felicitous	destitute, untoward
Chastise	punish, admonish	cheer, encourage
Kindred	relation, species	unrelated, dissimilar
Remnant	residue, piece	entire, whole
Intrigue	scheme, conspiracy	candor, sincerity
Yell	shout, shriek	whisper muted
Languid	sluggish, apathetic	energetic, spirited
Vivacious	spirited, energetic	dispirited, unattractive
Insipid	tasteless, vapid	delicious, luscious
Chaste	virtuous, pure	sullied, lustful
Tedious	wearisome. irksome	exhilarating, lively
Lavish	abundant, excessive	scarce, deficient
Insinuate	allude, hint	conceal, camouflage
Impute	attribute, ascribe	exculpate, support
Acumen	awareness, brilliance	stupidity, ignorance
Benevolent	benign, generous	malevolent, miserly
Vigilant	cautious, alert	careless, negligent
Deliberate	cautious, intentional	rash, sudden
Cheap	competitive, inexpensive	dear, unreasonable
Gracious	courteous, beneficent	rude, unforgiving
Clandestine	covert, furtive	open, legal
Subterfuge	deceit, stratagem	frankness, openness
Fluctuate	deflect, vacillate	stabilize, resolve
Treacherous	dishonest, duplicitous	forthright, reliable
Knave	dishonest, scoundrel	paragon, innocent
Creation	formation, foundation	destruction
Abjure	forsake, renounce	approve, sanction
Modest	humble, courteous	arrogant, pompous
Stranger	immigrant, guest	acquaintance, national
Sporadic	intermittent, scattered	incessant, frequent
Yearn	languish, crave	content, satisfy
Vilify	malign, slur, defame	cherish, commend
Ominous	menacing, foreboding	auspicious
Outrage	offence, maltreatment	praise, favour
Absolve	pardon, forgive	compel, accuse
Perverse	petulant, obstinate	complacent, docile
Reverence	respect, esteem	disrespect, affront
Hamper	retard, prevent	promote, facilitate
Quarantine	seclude, screen	befriend, socialize
Eccentric	strange, abnormal	natural, conventional
Infringe	violate, encroach	comply, concur
Concede	yield, permit	deny, reject
Pacify	appease, chasten	irritate, worsen
Gloomy	bleak, cloudy	gay, bright
Wary	cautious, circumspect	heedless, negligent

List of Synonyms and Antonyms in English

Words	Synonyms	Antonyms
Succinct	concise, terse	lengthy, polite
Deceit	deception, artifice	veracity, sincerity
Despair	depression, misery	contentment, hope
Offspring	descendant, sibling	ancestor, forefather
Inclination	disposition, affection	indifference, disinclination
Frugality	economy, providence	lavishness, extravagance
Conceit	egotism, immodesty	modesty
Venerable	esteemed, honored	unworthy, immature
Spurious	fake, counterfeit	genuine, authentic
Urchin	foundling, orphan	creep, knave
Jovial	frolicsome, cheerful	solemn, morose
Barbarous	frustrate, perplex	civilized
Bleak	grim, austere	bright, pleasant
Mutual	joint, identical	separate, distinct
Impartial	just, unbiased	prejudiced, biased
Linger	loiter, prolong	hasten, quicken
Niggardly	miser, covetous	generous, profuse
Shabby	miserable, impoverished	prosperous, thriving
Adversity	misfortune, calamity	prosperity, fortune
Progress	pace, betterment	retrogress, worsening
Conspicuous	prominent, obvious	concealed, hidden
Humility	resignation, fawning	boldness, pride
Awkward	rude, blundering	adroit, clever
Contempt	scorn, disregard	regard, praise
Impenitent	uncontrite, obdurate	repentant
Rout	vanquish, overthrow	succumb, withdraw
Tumultuous	violent, riotous	peaceful, harmonious
Reason	acumen, bounds	folly, speculation
Honor	adoration, reverence	denunciation, shame
Mitigate	alleviate, relieve	augment enhance
Trenchant	assertive, forceful	feeble, ambiguous
Confident	bold, undaunted	diffident, cowardly
Ascend	climb escalate	descend, decline
Camouflage	cloak, disguise	reveal
Obliging	complaisant, willing	mulish, obstinate
Fabricate	construct, produce	destroy, dismantle
Squalid	dirty, filthy	tidy, attractive
Blunt	dull, insensitive	keen, sharp
Pamper	flatter, indulge	deny, disparage
Baroque	florid, gilt	plain, unadorned
Abound	flourish, proliferate	deficient, destitute
Startled	frightened, shocked	waveringly
Terse	incisive, compact	diffuse, gentle
Decipher	interpret, reveal	misinterpret, distort
Impious	irreligious, unholy	pious, devout
Heretic	non-conformist, secularist	conformable, religious
Cement	plaster, mortar	disintegrate
Venom	poison, malevolence	antidote, benevolent
Prompt	precise, punctual	slow, negligent
Numerous	profuse, various	scarce, deficient
Rebellious	restless, attacking	submissive, compliant
Demolish	ruin, devastate	repair, construct
Lucid	sound, rational	obscure, hidden
Immerse	submerge, involve	emerge, uncover
Endeavour	undertake, aspire	cease, quit
Wilt	wither, perish	revive, bloom
Juvenile	young, tender	dotage, antiquated
Alleviate	abate, relieve	aggravate, enhance
Rectify	amend, remedy	falsify, worsen
Vain	arrogant, egoistic	modest
Throng	assembly, crowd	dispersion, sparsity
Captivate	beguile, bewitch	repel
Persuade	cajole, impress	dissuade, halt
Sacred	cherish, divine	ungodly, profane
Bustle	commotion, tumult	slowness, quiet
Adhere	comply, observe	condemn, disjoin
Temperate	cool, moderate	boisterous, violent
Wane	decline, dwindle	ameliorate, rise
Zest	delight, enthusiasm	disgust, passive
Deprive	despoil, divest	restore, renew
Glory	dignity, renown	shame, disgrace
Carnal	earthly, fleshly	spiritual
Eloquence	expression, fluency	halting, stammering
Calm	harmonious, unruffled	stormy, turbulent
Optimist	idealist	pessimist
Imminent	impending, brewing	distant, receding
Insolvent	indigent, destitute	wealthy, solvent
Batty	insane, silly	sane
Monotonous	irksome, tedious	varied, pleasant
Fanatical	narrow-minded, biased	liberal, tolerant
Morbid	nasty, macabre	healthy, cheerful
Hazard	peril, danger	conviction, security
Luxuriant	profuse, abundant	scanty, meagre
Redeem	recover, liberate	conserve lose
Jubilant	rejoicing, triumphant	melancholy, depressing
Classic	simple, typical	romantic, unusual
Obstinate	stubborn, adamant	pliable, flexible
Sympathy	tenderness, harmony	antipathy, discord
Bold	adventurous	timid
Calamity	adversity, misfortune	fortune
Haughty	arrogant, pompous	humble, submissive
Lure	attract, entice	repel, dissuade
Barrier	barricade, obstacle	link, assistance
Denounce	blame, boycott	defend
Stain	blemish, tarnish	honor, purify
Nullify	cancel, annual	confirm, uphold
Vicious	corrupt, obnoxious	noble, virtuous
Calumny	defamation, aspersion	commendation, praise
Fallacy	delusion, mistake	veracity, truth
Eradicate	destroy, exterminate	secure, plant
Restrain	detain, confine	incite
Dwarf	diminutive, petite	huge, giant
Zeal	eagerness, fervor	apathy, lethargy
Grudge	hatred, aversion	benevolence, affection
Arraign	incriminate, indict	exculpate, pardon
Irrepressible	irresistible, unconfined	composed, hesitant
Occult	latent, ambiguous	intelligible, transparent
Munificent	liberal, hospitable	frugal, penurious
Peerless	matchless, unrivalled	mediocre, commonplace
Abate	moderate, decrease	aggravate
Wan	pale, faded	bright, healthy
Immunity	prerogative, privilege	blame, censure
Ruthless	remorseless, inhumane	compassionate, lenient
Taciturn	reserved, silent	talkative, extrovert
Transient	temporal, transitory	lasting, enduring
Cease	terminate, desist	begin, originate
Jaded	tired, exhausted	renewed, recreated
Prudence	vigilance, discretion	indiscretion
Savage	wild, untamed	polished, civilized
Abolish	abrogate, annul	setup, establish
Liable	accountable, bound	unaccountable, apt to
Comic	clown, jester	tragic, tragedian
Decay	collapse, decompose	flourish, progress
Vouch	confirm, consent	repudiate, prohibit
Subsequent	consequent, following	preceding, previous
Gather	converge, huddle	disperse, dissemble
Justify	defend, exculpate	impute, arraign
Indigent	destitute, impoverished	rich, affluent
Ravage	destroy, ruin	reconstruct, renovate

A List of Synonyms and Antonyms

Words	Synonyms	Antonyms
Transparent	Diaphanous	opaque
Precarious	doubtful, insecure	assured
Hideous	frightful, shocking	attractive, alluring
Intrinsic	genuine, fundamental	extraneous, incidental
Acrimony	harshness, bitterness	courtesy, benevolence
Encumbrance	hindrance, obstacle	incentive, stimulant
Immense	huge, enormous	puny, insignificant
Captivity	imprisonment, confinement	freedom, liberty
Saucy	impudent, insolent	modest, humble
Urge	incite, implore	abhorrence, abomination
Nonchalant	indifferent, negligent	attentive, considerate
Callous	insensitive, indurated	kind, merciful
Miraculous	marvelous, extraordinary	ordinary, trivial
Spry	nimble, brisk	lethargic, sluggish
Callous	obdurate, unfeeling	compassionate, tender
Opaque	obscure, shady	transparent, bright
Frivolous	petty, worthless	solemn, significant
Bind	predicament	release
Quell	subdue, reduce	exacerbate, agitate
Wicked	vicious, immoral	virtuous, noble
Celebrated	acclaimed, lionized	unknown, inglorious
Mollify	appease, assuage	irritate, infuriate
Concur	approve, agree	differ, disagree
Uncouth	awkward, ungraceful	elegant, compensate
Noxious	baneful, injurious	healing, profitable
Coarse	bawdy, boorish	fine, chaste
Substantial	considerable, solid	tenuous, fragile
Defile	contaminate, pollute	purify, sanctity
Steep	course, lofty	flat, gradual
Guile	cunning, deceit	honesty, frankness
Knell	death knell, last blow	reconstruction, rediscovery
Abash	disconcert, rattle	uphold, discompose
Benign	favorable, friendly	malignant, cruel
Alien	foreigner, outsider	native, resident
Impediment	hurdle, obstruction	assistant, concurrence
Quack	impostor, deceiver	upright, unfeigned
Incompetent	inefficient, unskilled	dexterous, skilled
Veteran	ingenious, experienced	novice, tyro
Compassion	kindness, sympathy	cruelty, barbarity
Odious	malevolent, obnoxious	engaging, fascinating
Humble	meek, timid	proud, assertive
Sycophant	parasite, flatterer	devoted, loyal
Remorse	regret, penitence	ruthlessness, obduracy
Lax	slack, careless	firm, reliable
Yield	surrender abdicate	resist, protest
Placid	tranquil, calm	turbulent, hostile
Trivial	trifling, insignificant	significant, veteran
Inevitable	unavoidable, ascertained	unlikely, doubtful
Equivocal	uncertain, hazy	obvious, lucid
Fragile	weak, infirm	enduring, tough

About Author

Simi

Meet Simeron Khan, an experienced ESL teacher with a passion for guiding students towards fluency in English. With a knack for making even the most complex grammar rules accessible and enjoyable, Simeron has helped countless learners achieve their language goals.

Источник

NLTK Wordnet can be used to find synonyms and antonyms of words. NLTK Corpus package is used to read the corpus to understand the lexical semantics of the words within the document. A WordNet involves semantic relations of words and their meanings within a lexical database. The semantic relations within the WordNet are hypernyms, synonyms, holonyms, hyponyms, meronyms. NLTK WordNet includes the usage of synsets for finding the words within the WordNet with their usages, definitions, and examples. NLTK WordNet is to find the representations between senses. Relation type detection is connected to the WordNet with lexical semantics. A dog can be a mammal, and this can be expressed with an “IS-A” relation type sentence. Thus, NLTK Wordnet is used to find the relations between words from a document, spam detection, duplication detection, or characteristics of the words within a written text with their POS Tags.

NLTK Lemmatization, stemming, tokenization, and POS Tagging are related to the NLTK WordNet for Natural Language Processing. To use the Natural Language Tool Kit WordNet with better efficiency, the synonyms, and antonyms, holonyms, hypernyms, and hyponyms, and all of the lexical relations should be used for text processing and text cleaning. In this NLTK WordNet Python tutorial, the synonym and antonym finding, along with word similarity calculation will be used with NLTK Corpus Reader for the English Language.

A quick example of the synonym and antonym finding with NLTK Python can be found below.

def synonym_antonym_extractor(phrase):
     from nltk.corpus import wordnet
     synonyms = []
     antonyms = []

     for syn in wordnet.synsets(phrase):
          for l in syn.lemmas():
               synonyms.append(l.name())
               if l.antonyms():
                    antonyms.append(l.antonyms()[0].name())

     print(set(synonyms))
     print(set(antonyms))

synonym_antonym_extractor(phrase="word")

OUTPUT >>>

{'tidings', 'password', 'Holy_Writ', 'Good_Book', 'Bible', 'discussion', 'news', 'parole', 'give_voice', 'articulate', 'Son', 'word', 'Holy_Scripture', 'Book', 'give-and-take', 'Christian_Bible', 'intelligence', 'Logos', 'phrase', 'word_of_honor', 'formulate', 'Scripture', 'Word', 'watchword', 'countersign', 'Word_of_God'}
set()

The Synonym and Antonym finding example code block with Python NLTK involves a custom function creation, “nltk.corpus”, and “wordnet” with “syn.lemmas”, “syn.antonyms” along with a for a loop. The phrase “word” has been used as an example for the NLTK Synonym and Antonym finding. According to the WordNet within the NLTK.corpus, there is no antonym for “word” phrase, but the synonyms are “password”, “Holy Writ”, “Good Book”, “Bible”, “Discussion”, “News”, “Parole”. NLTK Synonyms and Antonyms involve lexical synonyms and contextual synonyms from WordNet.

In this Python and NLTK Synonym and Antonym finding guide, the usage of the NLTK WordNet for lexical semantics, word similarities, and synonym, antonym, hypernym, hyponym, verb frames, and more will be processed.

How to Find Synonyms of a Word with NLTK WordNet and Python?

To find the synonyms of a word with NLTK WordNet, the instructions below should be followed.

Import NLTK.corpus
Import WordNet from NLTK.Corpus
Create a list for assigning the synonym values of the word.
Use the “synsets” method.
use the “syn.lemmas” property to assign the synonyms to the list with a for loop.
Call the synonyms of the word with NLTK WordNet within a set.

An example of the finding of the synonym of a word via NLTK and Python is below.

from nltk.corpus import WordNet

synonyms = []

for syn in wordnet.synsets("love"):
    for i in syn.lemmas():
        synonyms.append(l.name())

print(set(synonyms))

OUTPUT >>>

{'dearest', 'love_life', 'get_it_on', 'roll_in_the_hay', 'lie_with', 'screw', 'bonk', 'passion', 'honey', 'sleep_together', 'lovemaking', 'making_love', 'make_love', 'have_sex', 'jazz', 'bed', 'erotic_love', 'dear', 'do_it', 'have_it_away', 'be_intimate', 'fuck', 'have_a_go_at_it', 'sleep_with', 'hump', 'enjoy', 'eff', 'have_it_off', 'know', 'have_intercourse', 'make_out', 'bang', 'beloved', 'love', 'get_laid', 'sexual_love'}

In the example above, the word “love” is used for finding its synonyms for different contexts with the NLTK and Python. The synonyms that are found for the “love” involves “dearest”, “lie with”, “screw”, “bonk”, “passion”, “honey” and some subtypes such as “sexual love”, “erotic love”. A word can be a synonym of another word, and indirectly related and connected words can be included within the synonym list of a word with NLTK WordNet. Thus, to find the different contextual synonyms and sibling phrases for a word, NLTK can be used. The compositional compounds and non-compositional compounds, or synonyms are used by the search engines. For a search engine optimization or search engine creation project, the NLTK WordNet and synonyms are prominent for understanding the context of textual data. Thus, from the Google Patents, the NLTK and WordNet can be found as mentioned methodology for synonym finding.

How to Find Antonyms of a Word with NLTK WordNet and Python?

To find the Antonyms of a Word with NLTK WordNet and Python, the following instructions should be followed.

Import NLTK.corpus
Import WordNet from NLTK.Corpus
Create a list for assigning the synonym values of the word.
Use the “synsets” method.
use the “syn.lemmas” property to assign the synonyms to the list with a for loop.
Use the “antonyms()” method with “name” property for calling the antonym of the phrase.
Call the antonyms of the word with NLTK WordNet within a set.

from nltk.corpus import wordnet

antonyms = []

for syn in wordnet.synsets("love"):
    for i in syn.lemmas():
         if i.antonyms():
              antonyms.append(i.antonyms()[0].name())

print(set(antonyms))

OPUTPUT >>>

{'hate'}

The antonym of the word “love” has been found as “hate” via the NLTK Antonym finding code example. Finding Synonyms and Antonyms from sentences by tokenizing the words within the sentence is beneficial to see the possible contextual connections to understand the content with NLP. Thus, creating a custom function for synonym finding within the text with Python is useful. The next section of the NLTK Python Synonym and Antonym Finding Tutorial with WordNet will be about a custom function creation.

How to use a custom Python Function for Finding Synonyms and Antonyms with NLTK WordNet?

To use a custom Python Function for finding synonyms and antonyms with NLTK, follow the instructions below.

Create a custom function with the Python built-in “def” command.
Use the text for synonym and antonym finding as the argument of the custom synonym and antonym finder Python function.
Import the “word_tokenize” from the “nltk.tokenize”.
Import the “wordnet” from the “nltk.corpus”.
Import “defualtdict” from the “collections”.
Import “pprint” for the pretty print the antonyms and synonyms.
Tokenize the words within the sentence for synonym and antonym finding with NLTK.
Create the antonym and synonym lists with “defaultdict(list)”.
Use a for loop with the tokens of tokenized sentence with NLTK for synonym and antonym finding.
Use a for a loop with the “synsets” for synonym and antonym finding.
Use an “if” statement to check whether the antonym of the word exists or not.
Use “pprint.pformat” and “dict” for making the synonym and antonym list writable to the a txt file.
Append all of the synonyms and antonyms for every word within the sentence with the created synonym and antonym defaultdict lists.
Open a new file as txt.
Print all of the synonyms and antonyms to a txt file.
Close the opened and created txt file.

An example of using the WordNet NLTK for finding synonyms and antonyms from an example sentence can be found below.

def text_parser_synonym_antonym_finder(text:str):
     from nltk.tokenize import word_tokenize
     from nltk.corpus import wordnet
     from collections import defaultdict
     import pprint

     tokens = word_tokenize(text)
     synonyms = defaultdict(list)
     antonyms = defaultdict(list)
     for token in tokens:
          for syn in wordnet.synsets(token):
               for i in syn.lemmas():
                    #synonyms.append(i.name())
                    #print(f'{token} synonyms are: {i.name()}')
                    synonyms[token].append(i.name())
                    if i.antonyms():
                         #antonyms.append(i.antonyms()[0].name())
                         #print(f'{token} antonyms are: {i.antonyms()[0].name()}')
                         antonyms[token].append(i.antonyms()[0].name())
     pprint.pprint(dict(synonyms))
     pprint.pprint(dict(synonyms))
     synonym_output = pprint.pformat((dict(synonyms)))
     antonyms_output = pprint.pformat((dict(antonyms)))
     with open(str(text[:5]) + ".txt", "a") as f:
          f.write("Starting of Synonyms of the Words from the Sentences: " + synonym_output + "n")
          f.write("Starting of Antonyms of the Words from the Sentences: " + antonyms_output + "n")
          f.close()

text_parser_synonym_antonym_finder(text="WordNet is a lexical database that has been used by a major search engine. From the WordNet, information about a given word or phrase can be calculated such as")


OUTPUT >>>

Starting of Synonyms of the Words from the Sentences: {'WordNet': ['wordnet',
             'WordNet',
             'Princeton_WordNet',
             'wordnet',
             'WordNet',
             'Princeton_WordNet'],
 'a': ['angstrom',
       'angstrom_unit',
       'A',
       'vitamin_A',
       'antiophthalmic_factor',
       'axerophthol',
       'A',
       'deoxyadenosine_monophosphate',
       'A',
       'adenine',
       'A',
       'ampere',
       'amp',
       'A',
       'A',
       'a',
       'A',
       'type_A',
       'group_A',
       'angstrom',
       'angstrom_unit',
       'A',
       'vitamin_A',
       'antiophthalmic_factor',
       'axerophthol',
       'A',
       'deoxyadenosine_monophosphate',
       'A',
       'adenine',
       'A',
       'ampere',
       'amp',
       'A',
       'A',
       'a',
       'A',
       'type_A',
       'group_A',
       'angstrom',
       'angstrom_unit',
       'A',
       'vitamin_A',
       'antiophthalmic_factor',
       'axerophthol',
       'A',
       'deoxyadenosine_monophosphate',
       'A',
       'adenine',
       'A',
       'ampere',
       'amp',
       'A',
       'A',
       'a',
       'A',
       'type_A',
       'group_A'],
 'about': ['about',
           'astir',
           'approximately',
           'about',
           'close_to',
           'just_about',
           'some',
           'roughly',
           'more_or_less',
           'around',
           'or_so',
           'about',
           'around',
           'about',
           'around',
           'about',
           'around',
           'about',
           'around',
           'about',
           'about',
           'almost',
           'most',
           'nearly',
           'near',
           'nigh',
           'virtually',
           'well-nigh'],
 'as': ['arsenic',
        'As',
        'atomic_number_33',
        'American_Samoa',
        'Eastern_Samoa',
        'AS',
        'angstrom',
        'angstrom_unit',
        'A',
        'vitamin_A',
        'antiophthalmic_factor',
        'axerophthol',
        'A',
        'deoxyadenosine_monophosphate',
        'A',
        'adenine',
        'A',
        'ampere',
        'amp',
        'A',
        'A',
        'a',
        'A',
        'type_A',
        'group_A',
        'equally',
        'as',
        'every_bit'],
 'be': ['beryllium',
        'Be',
        'glucinium',
        'atomic_number_4',
        'be',
        'be',
        'be',
        'exist',
        'be',
        'be',
        'equal',
        'be',
        'constitute',
        'represent',
        'make_up',
        'comprise',
        'be',
        'be',
        'follow',
        'embody',
        'be',
        'personify',
        'be',
        'be',
        'live',
        'be',
        'cost',
        'be'],
 'been': ['be',
          'be',
          'be',
          'exist',
          'be',
          'be',
          'equal',
          'be',
          'constitute',
          'represent',
          'make_up',
          'comprise',
          'be',
          'be',
          'follow',
          'embody',
          'be',
          'personify',
          'be',
          'be',
          'live',
          'be',
          'cost',
          'be'],
 'by': ['by', 'past', 'aside', 'by', 'away'],
 'calculated': ['calculate',
                'cipher',
                'cypher',
                'compute',
                'work_out',
                'reckon',
                'figure',
                'calculate',
                'estimate',
                'reckon',
                'count_on',
                'figure',
                'forecast',
                'account',
                'calculate',
                'forecast',
                'calculate',
                'calculate',
                'aim',
                'direct',
                'count',
                'bet',
                'depend',
                'look',
                'calculate',
                'reckon',
                'deliberate',
                'calculated',
                'measured'],
 'can': ['can',
         'tin',
         'tin_can',
         'can',
         'canful',
         'can',
         'can_buoy',
         'buttocks',
         'nates',
         'arse',
         'butt',
         'backside',
         'bum',
         'buns',
         'can',
         'fundament',
         'hindquarters',
         'hind_end',
         'keister',
         'posterior',
         'prat',
         'rear',
         'rear_end',
         'rump',
         'stern',
         'seat',
         'tail',
         'tail_end',
         'tooshie',
         'tush',
         'bottom',
         'behind',
         'derriere',
         'fanny',
         'ass',
         'toilet',
         'can',
         'commode',
         'crapper',
         'pot',
         'potty',
         'stool',
         'throne',
         'toilet',
         'lavatory',
         'lav',
         'can',
         'john',
         'privy',
         'bathroom',
         'can',
         'tin',
         'put_up',
         'displace',
         'fire',
         'give_notice',
         'can',
         'dismiss',
         'give_the_axe',
         'send_away',
         'sack',
         'force_out',
         'give_the_sack',
         'terminate'],
 'database': ['database'],
 'engine': ['engine',
            'engine',
            'locomotive',
            'engine',
            'locomotive_engine',
            'railway_locomotive',
            'engine'],
 'given': ['given',
           'presumption',
           'precondition',
           'give',
           'yield',
           'give',
           'afford',
           'give',
           'give',
           'give',
           'pay',
           'hold',
           'throw',
           'have',
           'make',
           'give',
           'give',
           'throw',
           'give',
           'gift',
           'present',
           'give',
           'yield',
           'give',
           'pay',
           'devote',
           'render',
           'yield',
           'return',
           'give',
           'generate',
           'impart',
           'leave',
           'give',
           'pass_on',
           'establish',
           'give',
           'give',
           'give',
           'sacrifice',
           'give',
           'pass',
           'hand',
           'reach',
           'pass_on',
           'turn_over',
           'give',
           'give',
           'dedicate',
           'consecrate',
           'commit',
           'devote',
           'give',
           'give',
           'apply',
           'give',
           'render',
           'grant',
           'give',
           'move_over',
           'give_way',
           'give',
           'ease_up',
           'yield',
           'feed',
           'give',
           'contribute',
           'give',
           'chip_in',
           'kick_in',
           'collapse',
           'fall_in',
           'cave_in',
           'give',
           'give_way',
           'break',
           'founder',
           'give',
           'give',
           'give',
           'afford',
           'open',
           'give',
           'give',
           'give',
           'give',
           'yield',
           'give',
           'give',
           'give',
           'give',
           'give',
           'give',
           'give',
           'give',
           'give',
           'give',
           'give',
           'given',
           'granted',
           'apt',
           'disposed',
           'given',
           'minded',
           'tending'],
 'has': ['hour_angle',
         'HA',
         'have',
         'have_got',
         'hold',
         'have',
         'feature',
         'experience',
         'receive',
         'have',
         'get',
         'own',
         'have',
         'possess',
         'get',
         'let',
         'have',
         'consume',
         'ingest',
         'take_in',
         'take',
         'have',
         'have',
         'hold',
         'throw',
         'have',
         'make',
         'give',
         'have',
         'have',
         'have',
         'experience',
         'have',
         'induce',
         'stimulate',
         'cause',
         'have',
         'get',
         'make',
         'accept',
         'take',
         'have',
         'receive',
         'have',
         'suffer',
         'sustain',
         'have',
         'get',
         'have',
         'get',
         'make',
         'give_birth',
         'deliver',
         'bear',
         'birth',
         'have',
         'take',
         'have'],
 'information': ['information',
                 'info',
                 'information',
                 'information',
                 'data',
                 'information',
                 'information',
                 'selective_information',
                 'entropy'],
 'is': ['be',
        'be',
        'be',
        'exist',
        'be',
        'be',
        'equal',
        'be',
        'constitute',
        'represent',
        'make_up',
        'comprise',
        'be',
        'be',
        'follow',
        'embody',
        'be',
        'personify',
        'be',
        'be',
        'live',
        'be',
        'cost',
        'be'],
 'lexical': ['lexical', 'lexical'],
 'major': ['major',
           'Major',
           'John_Major',
           'John_R._Major',
           'John_Roy_Major',
           'major',
           'major',
           'major',
           'major',
           'major',
           'major',
           'major',
           'major',
           'major',
           'major',
           'major'],
 'or': ['Oregon',
        'Beaver_State',
        'OR',
        'operating_room',
        'OR',
        'operating_theater',
        'operating_theatre',
        'surgery'],
 'phrase': ['phrase',
            'phrase',
            'musical_phrase',
            'idiom',
            'idiomatic_expression',
            'phrasal_idiom',
            'set_phrase',
            'phrase',
            'phrase',
            'give_voice',
            'formulate',
            'word',
            'phrase',
            'articulate',
            'phrase'],
 'search': ['search',
            'hunt',
            'hunting',
            'search',
            'search',
            'lookup',
            'search',
            'search',
            'search',
            'seek',
            'look_for',
            'search',
            'look',
            'research',
            'search',
            'explore',
            'search'],
 'such': ['such', 'such'],
 'used': ['use',
          'utilize',
          'utilise',
          'apply',
          'employ',
          'use',
          'habituate',
          'use',
          'expend',
          'use',
          'practice',
          'apply',
          'use',
          'use',
          'used',
          'exploited',
          'ill-used',
          'put-upon',
          'used',
          'victimized',
          'victimised',
          'secondhand',
          'used'],
 'word': ['word',
          'word',
          'news',
          'intelligence',
          'tidings',
          'word',
          'word',
          'discussion',
          'give-and-take',
          'word',
          'parole',
          'word',
          'word_of_honor',
          'word',
          'Son',
          'Word',
          'Logos',
          'password',
          'watchword',
          'word',
          'parole',
          'countersign',
          'Bible',
          'Christian_Bible',
          'Book',
          'Good_Book',
          'Holy_Scripture',
          'Holy_Writ',
          'Scripture',
          'Word_of_God',
          'Word',
          'give_voice',
          'formulate',
          'word',
          'phrase',
          'articulate']}
Starting of Antonyms of the Words from the Sentences: {'be': ['differ'],
 'been': ['differ'],
 'can': ['hire'],
 'given': ['take', 'starve'],
 'has': ['lack', 'abstain', 'refuse'],
 'is': ['differ'],
 'major': ['minor', 'minor', 'minor', 'minor', 'minor', 'minor', 'minor'],
 'used': ['misused']}

At the example above, a sentence has been used as an example for synonym and antonym finding with a custom Python function which is ” text_parser_synonym_antonym_finder”. Below, you can see the “txt” output of the synonym and antonym extractor from a sentence.

For the synonym and antonym finding and extraction from the text, we have created a new “.txt” file with the name of the first word of the sentence. It is important to notice that with NLTK WordNet and Python, a word can have multiple synonyms with the same word because there are different POS Tags for every word within the antonym and synonym list.

How to use POS Tagging for Synonym and Antonym Finding with NLTK WordNet?

To use POS Tagging for synonym and antonym finding with NLTK WordNet, the “pos” attribute should be used with the WordNet of NLTK. An example of usage for POS Tagging to find antonym and synonym with NLTK WordNet is below.

print("VERB of Love: ", wordnet.synsets("love", pos = wordnet.VERB))
print("ADJECTIVE of Love: ", wordnet.synsets("love", pos = wordnet.ADJ))
print("NOUN of Love: ", wordnet.synsets("love", pos = wordnet.NOUN))

OUTPUT >>>

VERB of Love:  [Synset('love.v.01'), Synset('love.v.02'), Synset('love.v.03'), Synset('sleep_together.v.01')]
ADJECTIVE of Love:  []
NOUN of Love:  [Synset('love.n.01'), Synset('love.n.02'), Synset('beloved.n.01'), Synset('love.n.04'), Synset('love.n.05'), Synset('sexual_love.n.02')]

The POS Tagging for Synonyms and Antonyms with NLTK WordNet shows different synsets (synonym rings) for different synonyms and antonyms of a word based on its context. For instance, the “love.v.01” and the “love.v.02” are not the same with each other in terms of context. To see the difference of a word in terms of its synonym meaning and context, the “definition” method of NLTK can be used with POS Tagging. To learn more about the NLTK POS Tagging, read the related guide and tutorial.

How to Find the Definition of a Synonym Word with NLTK WordNet?

To find the definition of a synonym Word with NLTK WordNet by understanding its context, the “wordnet.synset(“word example”, pos = wordnet.POS TAG).definition()” method should be used. To see the two different meanings of the same word as synonyms, the word “love” will use as an example below.

wordnet.synset("love.v.01").definition()

OUTPUT >>>

'have a great affection or liking for'

The example definition finding of a synonym of a word with NLTK WordNet above demonstrates the first verb example of the “love” as the “have a great affection or liking for”. The example below will show the second verb definition of “love”.

wordnet.synset("love.v.02").definition()

OUTPUT >>>

'get pleasure from'

The second meaning finding example of a word with NLTK WordNet can be found above. The second meaning of the word “love” is “get pleasure from”. Thus, even if the synonym of a word has the same “strings” as a “phrase”, still the meaning can be different. Thus, a word can have multiple synonyms with the same shape but different meanings. NLTK WordNet can be found by finding the different contexts, meanings of synonyms with the help of the POS Tagging with NLTK and the Definition Finding of a word. To improve the contextual understanding of a sentence with NLTK, the word usage examples can be called. Finding a word definition with Python has other methods such as using PyDictionary, but NLTK WordNet provides other benefits such as finding sentence examples for the words or finding different contexts of a word with its antonyms and synonyms.

How to find the sentence examples for words within NLTK WordNet?

To find the sentence examples with NLTK WordNet, the “wordnet.synset.examples()” method is used. An example of sentence example extraction with NLTK WordNet can be found below.

for i in wordnet.synset("love.v.01").examples():
     print(i)


OUTPUT >>>

I love French food
She loves her boss and works hard for him

In the example above, the first noun meaning of the “love” word is used with the “wordnet.synset().examples()” method. The “I love French Food” and “She loves her boss and works hard for him” sentences are examples of sentences that the word “love” is used with a specific meaning.

for i in wordnet.synset("love.v.01").examples():
     print(i)

OUTPUT >>>

I love cooking

The first meaning of “love” as a “verb” is used to take an example as above. The sentence “I love cooking” is returned by the NLTK WordNet as an example of the first meaning of the verb “love”. NLTK WordNet “examples()” method is useful to see the exact context of the specific word and its POS Tag with its versioned numeric value.

How to Extract the Synonyms and their Definitions at the same time with NLTK WordNet?

To extract the synonyms and their definitions with NLTK WordNet, the “wordnet.synset” and the “lemmas()” method with the “definition()” method should be used. The instructions below should be followed for extracting the synonyms and their definitions at the same time with NLTK WordNet.

Use the “wordnet.synset()” for a word such as “love”, or “phrase”.
Take the lemmas of the specific synonym ring with the “lemmas()” method.
Print the “lemma.name()” and “definition()” method at the same time.

Below, you can find the example output.

for i in wordnet.synsets("love"):
     for lemma in i.lemmas():
          print("Synonym of Word: " + lemma.name(), "| Definition of Synonym: " + i.definition())


OUTPUT >>>

Synonym of Word: love | Definition of Synonym: a strong positive emotion of regard and affection
Synonym of Word: love | Definition of Synonym: any object of warm affection or devotion
Synonym of Word: passion | Definition of Synonym: any object of warm affection or devotion
Synonym of Word: beloved | Definition of Synonym: a beloved person; used as terms of endearment
Synonym of Word: dear | Definition of Synonym: a beloved person; used as terms of endearment
Synonym of Word: dearest | Definition of Synonym: a beloved person; used as terms of endearment
Synonym of Word: honey | Definition of Synonym: a beloved person; used as terms of endearment
Synonym of Word: love | Definition of Synonym: a beloved person; used as terms of endearment
Synonym of Word: love | Definition of Synonym: a deep feeling of sexual desire and attraction
Synonym of Word: sexual_love | Definition of Synonym: a deep feeling of sexual desire and attraction
Synonym of Word: erotic_love | Definition of Synonym: a deep feeling of sexual desire and attraction
Synonym of Word: love | Definition of Synonym: a score of zero in tennis or squash
Synonym of Word: sexual_love | Definition of Synonym: sexual activities (often including sexual intercourse) between two people
Synonym of Word: lovemaking | Definition of Synonym: sexual activities (often including sexual intercourse) between two people
Synonym of Word: making_love | Definition of Synonym: sexual activities (often including sexual intercourse) between two people
Synonym of Word: love | Definition of Synonym: sexual activities (often including sexual intercourse) between two people
Synonym of Word: love_life | Definition of Synonym: sexual activities (often including sexual intercourse) between two people
Synonym of Word: love | Definition of Synonym: have a great affection or liking for
Synonym of Word: love | Definition of Synonym: get pleasure from
Synonym of Word: enjoy | Definition of Synonym: get pleasure from
Synonym of Word: love | Definition of Synonym: be enamored or in love with
Synonym of Word: sleep_together | Definition of Synonym: have sexual intercourse with
Synonym of Word: roll_in_the_hay | Definition of Synonym: have sexual intercourse with
Synonym of Word: love | Definition of Synonym: have sexual intercourse with
Synonym of Word: make_out | Definition of Synonym: have sexual intercourse with
Synonym of Word: make_love | Definition of Synonym: have sexual intercourse with
Synonym of Word: sleep_with | Definition of Synonym: have sexual intercourse with
Synonym of Word: get_laid | Definition of Synonym: have sexual intercourse with
Synonym of Word: have_sex | Definition of Synonym: have sexual intercourse with
Synonym of Word: know | Definition of Synonym: have sexual intercourse with
Synonym of Word: do_it | Definition of Synonym: have sexual intercourse with
Synonym of Word: be_intimate | Definition of Synonym: have sexual intercourse with
Synonym of Word: have_intercourse | Definition of Synonym: have sexual intercourse with
Synonym of Word: have_it_away | Definition of Synonym: have sexual intercourse with
Synonym of Word: have_it_off | Definition of Synonym: have sexual intercourse with
Synonym of Word: screw | Definition of Synonym: have sexual intercourse with
Synonym of Word: fuck | Definition of Synonym: have sexual intercourse with
Synonym of Word: jazz | Definition of Synonym: have sexual intercourse with
Synonym of Word: eff | Definition of Synonym: have sexual intercourse with
Synonym of Word: hump | Definition of Synonym: have sexual intercourse with
Synonym of Word: lie_with | Definition of Synonym: have sexual intercourse with
Synonym of Word: bed | Definition of Synonym: have sexual intercourse with
Synonym of Word: have_a_go_at_it | Definition of Synonym: have sexual intercourse with
Synonym of Word: bang | Definition of Synonym: have sexual intercourse with
Synonym of Word: get_it_on | Definition of Synonym: have sexual intercourse with
Synonym of Word: bonk | Definition of Synonym: have sexual intercourse with

The example above is for every variation of the word “love” with its possible synonyms, and their contexts. It shows that how content can be made richer with certain types of vocabularies, and how the context can be deepened further for improving the relevance. A possible Information Retrieval system can understand the content’s purpose with these synonyms and antonyms further. Thus, NLTK WordNet and synonym, antonym extraction along with examining the word’s definition and example sentences are important.

How to extract synonyms and antonyms from other languages besides English via NLTK Wordnet?

To extract synonyms and antonyms from other languages besides English via NLTK Wordnet, the “langs()” method should be used. With NLTK WordNet and the “lang” method, the ISO-639 Language Codes should be used. ISO-639 language codes contain the language codes with a shortcut. The language codes that can be used with NLTK WordNet can be seen below.

To use the ISO-639 Language codes with NLTK WordNet to find synonyms and antonyms with the “lang” attribute, you can examine the example below.

wordnet.synset("love.v.01").lemma_names("fra")

OUTPUT >>>

['aimer', 'amour', 'bien', "faire_l'amour", 'Mange']

The example use of the “lang” method to find the synonym of “love” with the first verb meaning within the French language can be seen above. The synonyms of “love” as a verb within French can be seen below.

These types of language translations with different synonyms from different contexts can be used to find the contextual relevance between different documents from different languages. Thus, NLTK is a valuable tool for search engines. And, the ISO-639 Language Codes have been used for hreflang attribute in the context of SEO as in NLTK WordNet “lang” method.

What other Lexical Semantics can be extracted with NLTK WordNet besides Antonyms and Synonyms?

The other lexical semantics can be extracted with NLTK WordNet besides antonyms and synonyms are listed below.

Hypernyms: Hypernym is the opposite (antonym) of the Hyponym. Hypnerym is the superior thing of a class of things. NLTK WordNet can be used for extracting the hypernyms of a word with the “hypnerym” attribute.
Hyponyms: Hyponym is the opposite (antonym) of the Hypernym. Hyponym is the interior thing of a class of things. NLTK WordNet can be used for extracting the hyponym of a word with the “hyponym” attribute.
Holonyms: Holonym is the opposite (antonym) of the Meronym. Holonym is the name of the whole thing that has multiple parts. NLTK WordNet can be used for extracting the hypernyms of a word with the “member_holonym” attribute.
Meronyms: Meronym is the opposite (antonym) of the Holonym. It represents the part name within the thing. NLTK WordNet can be used for extracting the hypernyms of a word with the “hypnerym” attribute. NLTK WordNet has the “member_meronyms” for extracting the meronym of a word.

Lexical Semantics involves hypernyms, hyponyms, holonyms, meronyms, antonyms, synonyms, and more semantic word relations. Semantic Role Labeling and Lexical Semantics are directly connected to Semantic SEO and Natural Language Processing. In this context, NLTK WordNet and Lexical Relations such as hypernyms, hyponyms, meronyms are important for SEO and NLP.

How to Find Hypernym of a Word with NLTK WordNet and Python?

To find the Hypernyms of a word and to see its superior class names, the “hypernym()” method within the NLTK WordNet and Synset should be used. The Hypernym is a part of Lexical Relations in NLTK WordNet that explains a word’s upper and superior concepts. A hypernym can show the context of the word. An example of finding the hypernym of a word can be seen below.

for syn in wordnet.synsets("love"):
     print(syn.hypernym_distances())

OUTPUT >>>

{(Synset('feeling.n.01'), 2), (Synset('attribute.n.02'), 4), (Synset('love.n.01'), 0), (Synset('entity.n.01'), 6), (Synset('abstraction.n.06'), 5), (Synset('state.n.02'), 3), (Synset('emotion.n.01'), 1)}
{(Synset('love.n.02'), 0), (Synset('cognition.n.01'), 3), (Synset('content.n.05'), 2), (Synset('psychological_feature.n.01'), 4), (Synset('entity.n.01'), 6), (Synset('abstraction.n.06'), 5), (Synset('object.n.04'), 1)}
{(Synset('whole.n.02'), 5), (Synset('physical_entity.n.01'), 7), (Synset('entity.n.01'), 8), (Synset('entity.n.01'), 5), (Synset('organism.n.01'), 3), (Synset('object.n.01'), 6), (Synset('beloved.n.01'), 0), (Synset('living_thing.n.01'), 4), (Synset('physical_entity.n.01'), 4), (Synset('lover.n.01'), 1), (Synset('person.n.01'), 2), (Synset('causal_agent.n.01'), 3)}
{(Synset('abstraction.n.06'), 6), (Synset('state.n.02'), 4), (Synset('sexual_desire.n.01'), 1), (Synset('attribute.n.02'), 5), (Synset('entity.n.01'), 7), (Synset('love.n.04'), 0), (Synset('feeling.n.01'), 3), (Synset('desire.n.01'), 2)}
{(Synset('score.n.03'), 1), (Synset('measure.n.02'), 4), (Synset('number.n.02'), 2), (Synset('entity.n.01'), 6), (Synset('abstraction.n.06'), 5), (Synset('love.n.05'), 0), (Synset('definite_quantity.n.01'), 3)}
{(Synset('sexual_activity.n.01'), 1), (Synset('organic_process.n.01'), 3), (Synset('process.n.06'), 4), (Synset('sexual_love.n.02'), 0), (Synset('entity.n.01'), 6), (Synset('physical_entity.n.01'), 5), (Synset('bodily_process.n.01'), 2)}
{(Synset('love.v.01'), 0)}
{(Synset('like.v.02'), 1), (Synset('love.v.02'), 0)}
{(Synset('love.v.03'), 0), (Synset('love.v.01'), 1)}
{(Synset('copulate.v.01'), 1), (Synset('sleep_together.v.01'), 0), (Synset('connect.v.01'), 3), (Synset('join.v.04'), 2)}

The explanation of “how to find hypernym of a word with NLTK” code block is below.

Import NLTK and WordNet
Use “.synsets” method of wordnet.
Use a for loop for all of the contexts of the phrases.

The example of discovering the hypernyms of the selected phrase represents different “noun” and “verb” contexts. Thus, there are many different hypernym paths. The hypernym distance represents different conceptual connections with a meaningful lexical hierarchy. For instance, the phrase “love” has “feeling” as hypernym, feeling with the first “noun” context while “attribute” is the second hypernym example for the second “noun” context. The context of the words can be seen with its definition as below.

wordnet.synset("love.n.01").definition()

OUTPUT>>>

'a strong positive emotion of regard and affection'

WordNet says that the “love.n.01” means a strong positive emotion. Thus, the hypernym of the word “love” for the first noun context is “feeling” which is a synonym of “emotion”. For the hypernym of the second context which is the second “noun” version of the “love”, the example is below.

wordnet.synset("love.n.02").definition()

OUTPUT >>>

'any object of warm affection or devotion'

The word “attribute” is the hypernym of the word “love” for the second noun meaning which is “any object of warm affection or devotion”. Thus, according to the context of a word, the meaning and the hypernyms will change. The WordNet hypernym paths and distances can affect the topicality score and semantic relevance of a content piece to a query or a context. Another “hypernym” finding example can be found below.

dog = wordnet.synset('dog.n.01')
print(dog.hypernyms())

OUTPUT >>>

[Synset('basenji.n.01'), Synset('corgi.n.01'), Synset('cur.n.01'), Synset('dalmatian.n.02'), Synset('great_pyrenees.n.01'), Synset('griffon.n.02'), Synset('hunting_dog.n.01'), Synset('lapdog.n.01'), Synset('leonberg.n.01'), Synset('mexican_hairless.n.01'), Synset('newfoundland.n.01'), Synset('pooch.n.01'), Synset('poodle.n.01'), Synset('pug.n.01'), Synset('puppy.n.01'), Synset('spitz.n.01'), Synset('toy_dog.n.01'), Synset('working_dog.n.01')]

The phrase “dog” with the first noun meaning has different hypernyms from “dalmatian” to the “griffon” or “puppy”, and “working dog”. All those hypernyms can be closer to the meaning of the dog within the document according to the general context of the document. Finding hypernyms and the hyponyms are connected to each other. Hyponyms can complete the meaning of a hypernym for the selected phrase within the NLTK WordNet.

How to Find Hyponym of a Word with NLTK WordNet and Python?

To find hyponyms of a word with NLTK WordNet and Python, the “hyponyms()” method can be used. Hyponym finding is beneficial to see the lexical relations of a word as a hypernym. Hyponyms comprise the inferior types of inferior versions of a specific phrase with different contexts. To find hyponyms with NLTK and NLP, follow the instructions below.

for syn in wordnet.synsets("love"):
     print(syn.hyponyms())

OUTPUT >>>

[Synset('agape.n.01'), Synset('agape.n.02'), Synset('amorousness.n.01'), Synset('ardor.n.02'), Synset('benevolence.n.01'), Synset('devotion.n.01'), Synset('filial_love.n.01'), Synset('heartstrings.n.01'), Synset('lovingness.n.01'), Synset('loyalty.n.02'), Synset('puppy_love.n.01'), Synset('worship.n.02')]
[]
[]
[]
[]
[]
[Synset('adore.v.01'), Synset('care_for.v.02'), Synset('dote.v.02'), Synset('love.v.03')]
[Synset('get_off.v.06')]
[Synset('romance.v.02')]
[Synset('fornicate.v.01'), Synset('take.v.35')]

The explanation of the hyponym finding with the NLTK code example is below.

Import the NLTK and WordNet
Call the “wordnet.sysnset” for the selected phrase.
Call every “hyponym” for every context of the word.

The example above for the phrase “love” shows that there are different types of hyponyms for different types of meanings of “love”. For the first noun context, the hyponym of love is “agape”. “Agape” is a hyponym for the second meaning of “love” as a noun at the same time. In WordNet, a word can have different hypernyms for different noun versions while having the same hyponym for both of them such as love. There can be multiple hyponyms for a specific word within the NLTK such as “amorousness”. Amarousness is the hyponym of “love” for the first noun meaning. It means that when we check the hypernym of a hyponym, the same concept will appear to complete the hypernym path. An example of bidirectional hypernym-hyponym control for NLTK WordNet is below.

for syn in wordnet.synsets("amorousness"):
     print(syn.hypernyms())

OUTPUT >>>

[Synset('love.n.01')]
[Synset('sexual_desire.n.01')]

The hypernym of the “amorousness” is the phrase “love”. And, the second hypernym of the “amorousness” is the “sexual desire” which is a signal of the connection’s context between the “love” and the “amorousness”. The same process can be followed for the meaning of the first hyponym of love which is “agape”.

for syn in wordnet.synsets("agape"):
     print(syn.hypernyms())

OUTPUT >>>

[Synset('love.n.01')]
[Synset('love.n.01')]
[Synset('religious_ceremony.n.01')]
[]

“Agape” has the “love” as the hypernym naturally. It has “religious ceremony” as a hypernym as well which shows the context of the connection to the phrase “love”. If we check the synonyms and the definition of “agape”, this connection will be more clear.

wordnet.synset("agape.n.01").definition()


OUTPUT >>>

'(Christian theology) the love of God or Christ for mankind'

The definition of the “agape” shows the “religious ceremony” connection for the word “love” and its hyponym. The synonyms of the “agape” can make this connection’s context more clear.

for syn in wordnet.synsets("agape"):
          for l in syn.lemmas():
               print(l.name())

OUTPUT >>>

agape
agape
agape_love
agape
love_feast
agape
gaping

The synonyms of the “agape” represent its “Christian Love” context as a hyponym for the word “love”. Because the “love feast” is one of the synonyms of the word “love”. And, the “love feast” is actually a term for Christian Mythology.

“Love feast” is defined with the word “agape”.

The NLTK WordNet Hypernyms and Hyponyms show the context of the word and the possible topicality association of the concept. Hyponym finding via NLTK and NLP can be supported by auditing the hypernyms and synonyms, along with the definitions of the words. Topic Modeling is an important part of the NLTK Hypernym and Hyponym connections. In this context, the Topic Modeling with Bertopic can be given as an example.

How to Find Verb Frames of a Verb with NLTK WordNet and Python?

To find the verb frames of a verb with NLTK WordNet can be found with the “frame_ids” and “frame_strings” methods. A verb-frame involves the meaning of the specific verb with an example sentence. Below, you can see an example usage of the “frame_ids” and “frame_strings” with NLTK WordNet to find the verb frames.

for lemma in wordnet.synset('run.v.02').lemmas():
     print(lemma, lemma.frame_ids())
     print(" | ".join(lemma.frame_strings()))

OUTPUT >>>

Lemma('scat.v.01.scat') [1, 2, 22]
Something scat | Somebody scat | Somebody scat PP
Lemma('scat.v.01.run') [1, 2, 22]
Something run | Somebody run | Somebody run PP
Lemma('scat.v.01.scarper') [1, 2, 22]
Something scarper | Somebody scarper | Somebody scarper PP
Lemma('scat.v.01.turn_tail') [1, 2, 22]
Something turn_tail | Somebody turn_tail | Somebody turn_tail PP
Lemma('scat.v.01.lam') [1, 2, 22]
Something lam | Somebody lam | Somebody lam PP
Lemma('scat.v.01.run_away') [1, 2, 22]
Something run_away | Somebody run_away | Somebody run_away PP
Lemma('scat.v.01.hightail_it') [1, 2, 22]
Something hightail_it | Somebody hightail_it | Somebody hightail_it PP
Lemma('scat.v.01.bunk') [1, 2, 22]
Something bunk | Somebody bunk | Somebody bunk PP
Lemma('scat.v.01.head_for_the_hills') [1, 2, 22]
Something head_for_the_hills | Somebody head_for_the_hills | Somebody head_for_the_hills PP
Lemma('scat.v.01.take_to_the_woods') [1, 2, 22]
Something take_to_the_woods | Somebody take_to_the_woods | Somebody take_to_the_woods PP
Lemma('scat.v.01.escape') [1, 2, 22]
Something escape | Somebody escape | Somebody escape PP
Lemma('scat.v.01.fly_the_coop') [1, 2, 22]
Something fly_the_coop | Somebody fly_the_coop | Somebody fly_the_coop PP
Lemma('scat.v.01.break_away') [1, 2, 22]
Something break_away | Somebody break_away | Somebody break_away PP

The example above demonstrates how to find the different meanings of a verb with its variations. The second meaning of the verb “run” has other variations and synonyms such as “turn_tail”, “scat”, “breakaway”, “escape” and other contextual synonyms. The verb frames are helpful to find the possible word replacements and contextual connections between the sentences. If the specific verb is replaced by one of the examples within the verb frame without changing the meaning of the sentence or the context of the paragraph, it means that the verb frames are used properly.

How to Find Similar Words for a targeted Word with NLTK WordNet and Python?

To find similar words to each other with NLTK Wordnet and Python, the “lch_similarity” and the “path_similarity” are used. The NLTK WordNet measures the word similarity based on the hypernym and hyponym taxonomy. The distance between the words within the hypernym and hyponym paths represents the similarity level between them. The similarity types and methods that can be used within the NLTK WordNet to measure the word similarity are listed below.

Resink Similarity with “synset1.res_similarity(synset2, ic)”.
Wu-Palmer Similarity with “synset1.wup_similarity(synset2)”.
Leacock-Chodorow Similarity with “synset1.lch_similarity(synset2)”.
Path Similarity with “synset1.path_similarity(synset2)”.

Example measurement of the word similarity with NLTK WordNet can be found below.

wordnet.synset("dog.n.01").path_similarity(wordnet.synset("cat.n.01"))

OUTPUT >>>

0.2

The word similarity score within the NLTK WordNet represents the similarity between the words. The word similarity score within NLTK WordNet is between 0 and 1. 0 represents there is no similarity, while 1 represents the exact identical similarity. Thus, the example measurement for word similarity with Python above shows that the word “cat” and word “dog” as “noun” are similar to each other 20%.

The “Leacock-Chodorow Similarity” takes the hypernym and hyponym distance for the similarity calculation while taking the shortest path into account. The shortest hypernym and hyponym path between two words and the total depth of the path will represent the similarity for Leacock-Chodorow similarity measurement. Below, you can find example usage of the Leacock-Chodorow Similarity with NLTK WordNet.

wordnet.synset("dog.n.01").lch_similarity(wordnet.synset("cat.n.01"))

OUTPUT >>>

2.0281482472922856

The example above shows the score of the word similarity based on the Leacock-Chodorow Similarity with NLTK WordNet. Finding similar words with Python and NLTK WordNet is a broad topic that can be handled with formulas like “-log(p/2d)” and other similarity measurements, or root node attributes. It is useful to see the word predictions and replacements with success. An NLP algorithm can replace the words based on their similarity to check the context shifts. If the context shifts too much, it means that the content is relevant to the first context candidate. And, word similarity with NLTK can be used for relevance calculation, or Information Retrieval systems.

How to Find Topic Domains of a Word with NLTK WordNet and Python?

NLTK WordNet has a “topic domain” metric for a specific word. The topic domain shows the word’s context and its value for a knowledge domain. The NLTK WordNet can be used to understand the topicality and topical relevance of content to another. All of the document’s from a website, or a book or all of the sentences from content with their words can be taken to calculate the topic domains. The dominant topic domain can signal the main context of the document. For a search engine, thus NLTK WordNet, or Semantic Networks with a proper dataset is useful.

To find the topic domains of a word with NLTK WordNet, and Python follow the steps below.

Import the NLTK.corpus and wordnet to find the topic domain.
Choose an example word or phrase to take the topic domain.
Use the “synset” method of Wordnet for the chosen word.
Use the “topic_domains()” method of the “synset” object.
Read the output of the “topic_domains()” example.

Example usage of the NLTK WordNet to find the topic domain of a word can be found below.

wordnet.synset('code.n.03').topic_domains()

OUTPUT >>>

[Synset('computer_science.n.01')]

The example above shows that the topic domain of the word “code” as a noun with the third version is “computer science”. One of the problems for diagnosing the topic domains for words from NLTK WordNet is that the topic modeling and hierarchy might not be detailed enough. To make it up, the Wordnet Domains can be used. To use the WordNet Domains, an application is necessary with the email address and accepting the Creative Common Licence. With the WordNet Domains, more than 400 topic domains can be explored. To print the topic domains within the WordNet Domains, use the code example below.

from collections import defaultdict
from nltk.corpus import wordnet as wn

domain2synsets = defaultdict(list)
synset2domains = defaultdict(list)
for i in open('wn-domains-3.2-20070223', 'r'):
    ssid, doms = i.strip().split('t')
    doms = doms.split()
    synset2domains[ssid] = doms
    for d in doms:
        domain2synsets[d].append(ssid)

for ss in wn.all_synsets():
    ssid = str(ss.offset).zfill(8) + "-" + ss.pos()
    if synset2domains[ssid]:
        print( ss, ssid, synset2domains[ssid])

for dom in sorted(domain2synsets):
    print(dom, domain2synsets[dom][:3])


OUTPUT >>>


acoustics ['02584104-n', '02584268-n', '02584812-n']
administration ['00045146-n', '00556291-n', '00556427-n']
agriculture ['00429923-n', '00866914-n', '00996641-n']
anatomy ['00037703-n', '00133136-n', '00353921-n']
animal_husbandry ['00792299-n', '00860674-n', '00861073-n']
animals ['00012748-n', '00962111-n', '01153586-n']
anthropology ['00210724-n', '00211160-n', '00211365-n']
applied_science ['03985477-n', '04266345-n', '04352832-n']
archaeology ['00040040-n', '01328460-n', '01891224-n']
archery ['00423600-n', '09181370-n', '09608089-n']
architecture ['00577011-n', '00871831-n', '02578017-n']
art ['00258392-n', '00573836-n', '00672395-n']
artisanship ['00869978-n', '00870256-n', '00870389-n']
astrology ['03407158-n', '04436236-n', '05444230-n']
astronautics ['00280016-n', '02827728-n', '02966235-n']
astronomy ['00045801-n', '02655846-n', '02656041-n']
athletics ['00410707-n', '00410925-n', '00414898-n']
atomic_physic ['02657581-n', '02685588-n', '02736848-n']
aviation ['00047580-n', '00047871-n', '00159777-n']
badminton ['00455850-n', '00456227-n', '00458699-n']

Finding topics within the documents with the topic domains of the words via NLTK WordNet can be done in a better way by using the WordNet Domains. Below, you can see the output of the WordNet Domains with Python.

Google Search Engine has a similar topicality and topic domain understanding to the NLTK WordNet and the WordNet Domains. Google NLP API gives more than 100 topics for a specific section. In this context, reading using the Google Knowledge Graph API and Python tutorial and guideline is beneficial to see the topics, entities, and their classification based on the text.

To learn more, read the WordNet Domains Guideline.

How to Find Region Domains of a Word with NLTK WordNet and Python?

Region domains represent the region of the specific word that is used. It is useful to see the cultural affinity of the word. A region domain can signal the topic domain. But, the difference between the region domain and the topic domain is that it represents the geographical and cultural category more than its main topic. To find the region domain with NLTK WordNet, the “region_domains()” method is used. The instructions to find the region domains of a word with NLTK WordNet are below.

Import the NLTK Corpus and WordNet to find the region domain of a word.
Choose a word to find the region domains.
Use the “WordNet.synset()” for the example word.
Use the “region_domains()” method.

An example of finding region domains with NLTK WordNet and Python can be found below.

wordnet.synset('pukka.a.01').region_domains()

OUTPUT >>>

[Synset('india.n.01')]

The example above shows that the word “Pukka” as an adjective has India as the region domain. The same process can be implemented for all of the words from a document to find the overall region signals of a document with NLTK WordNet.

The topic domain and region domain difference is that the topic domain focuses on the meaning of the word while the region domain focuses on the word’s geography and culture. Similarly, the “usage domain” focuses on which language style uses the specific word. For instance, a word can be from a medicine topic, and Japan as a region while being used in scientific language. Thus, NLTK WordNet is to provides information for exploring the language tonality, region signals, and topicality understanding. The next section will demonstrate an example for the NLTK WordNet usage domains.

How to Find Usage Domains of a Word with NLTK WordNet and Python?

Usage domain involves the word’s used language style. A word can be used by scientists, or it can be used within the slang language. To learn the content’s authenticity, target audience, or the author’s writing character, the usage domain can be used. In this context, the accent of a textual language can be seen. To find the usage domain of a word with the NLTK WordNet, the “usage_domains()” method should be used. The instructions for finding usage domains with NLTK WordNet are below.

Import the NLTK Corpus and WordNet
Choose a word to find the usage domains.
Use the “WordNet.synset()” for the word.
Use the “usage_domains()” method.

Example usage for the NLTK WordNet usage domain finding is below.

wn.synset('fuck.n.01').usage_domains()

OUTPUT >>>

[Synset('obscenity.n.02'), Synset('slang.n.02')]

The example of finding the usage domain of a word with NLTK WordNet and Python above demonstrates a word’s usage domain from “obscenity” and the “slang” language. NLTK WordNet usage domains can be a good signal to see the overall content character of a website, or a document and book.

How to Use WordNet for other languages with Python NLTK?

To use the WordNet NLTK within another language, the “wordnet.lang”, or “lemma_names” method is used. The ISO-639 language codes are used to identify the language that will be used for the WordNet NLTK. Below, you can find example usage of NLTK WordNet for other languages to find the synonyms or the antonyms along with other lexical relations with Python.


wordnet.synset("love.v.01").lemma_names("jpn")

OUTPUT >>>

['いとおしむ',
 'いとおしがる',
 '傾慕+する',
 '好く',
 '寵愛+する',
 '愛しむ',
 '愛おしむ',
 '愛好+する',
 '愛寵+する',
 '愛慕+する',
 '慕う',
 'ほれ込む']

The example of finding the synonyms for the word “love” within Japan with NLTK Wordnet and Python can be seen above. NLTK WordNet can be used for finding synonyms and lemmas of English Words via words from other languages. The example below shows how to find the synonyms of the word “macchina” in English which is Italian.

wordnet.lemmas('macchina', lang='ita')

OUTPUT >>>

[Lemma('car.n.01.macchina'),
 Lemma('locomotive.n.01.macchina'),
 Lemma('machine.n.01.macchina'),
 Lemma('machine.n.02.macchina')]

Using other language words for finding synonyms within the English language via NLTK WordNet is useful to see the possible connections within the English from other languages. A word from Italian can have different types of lexical relations within English. The cross-language synonym finding shows the understanding of the semantics in a language-agnostic way. Thus, using NLTK WordNet for multi-language applications such as search engines are useful to see a topic with more layer.

The NLTK WordNet-related other NLTK tasks for NLP can be found below.

NLTK Tokenize is related to NLTK WordNet, because every word that is tokenized via NLTK can be audited with its hypernyms, hyponyms or synonyms within the WordNet.
NLTK Lemmatize is related to NLTK WordNet as an NLP Task because it provides the different variations and versions of the same word to understand its context.
NLTK Stemming is related NLTK WordNet task for NLP because it gives the different stemmed versions of the words.
NLTK Part of Speech Tag is related to NLTK WordNet as NLP task because it gives the different roles for a word within a sentence by protecting its context.

Related terms to the WordNet from NLTK comprise the lexical relations and semantic relevance along with the similarity. Natural Language Toolkit for a WordNet is connected to the terms below.

FrameNet: FrameNet is connected to the NLTK WordNet bcause it involves the semantic role labels based on the predicates of the sentences and their meanings.
Lexical Relations: Lexical relations is connected to WordNet NLTK because it provides lexical similarities and connections between different terms and concepts.
Semantic Relevance: Semantic Relevance is connected to NLTK WordNet because it shows how a word is relevant to another one based on semantic relations.
Semantic Similarity: Semantic Similarity is connected to NLTK because it provides similarity between two words based on their contexts.
Hypernyms: Hypernyms is connected to WordNet because it involes the upper and superior parts of a word.
Hyponyms: Hyponyms is connected to WordNet bcause it involves the inferior and lower parts of a word.
Synonyms: Synonyms is connected to WordNet bcause it involves the other words that have the same meaning.
Antonyms: Antonyms is connected to WordNet bcause it involves the opposite meaning words of a word.
Holonyms: Holonyms is connected to WordNet bcause it involves the whole of a thing.
Meronyms: Holonyms is connected to WordNet bcause it involves the sub-part of a thing.
Partonym: Partonym is connected to WordNet bcause it involves the change of a word to another one with different suffixes or prefixes.
Polysemy: Polysemy is connected to WordNet bcause it provides same phrases with different meanings.
Natural Language Processing is connected to WordNet bcause it is the process of understanding human language with machines.
Semantic Search is connected to WordNet bcause it provides meaningful connections between different words within a semantic map.
Semantic SEO is connected to WordNet bcause WordNet can be used for better content writing practices.
Semantic Web is connected to WordNet bcause semantic web behavior patterns have meaningful word relations.
Named Entity Recognition is connected to WordNet bcause it provides recognition of the named entities.

Last Thoughts on NLTK WordNet and Holistic SEO

NLTK WordNet and Holistic SEO should be used together. The Holistic SEO contains every vertical and angle of the search engine optimization. NLTK WordNet can provide different contexts for a specific word for an SEO to check the possible contextual connections between different phrases. NLTK WordNet is a prominent tool to understand the text along with text cleaning and text processing. Google and other semantic search engines such as Microsoft Bing can use synonyms, antonyms, and hypernyms or hyponyms for query rewriting. A search engine can process a query while tokenizing it and replacing the words with other related words with different contexts. NLTK WordNet can understand the topical relevance of a specific content piece to a query, or query cluster. Based on this, NLTK WordNet and Holistic SEO should be taken and processed together.

The NLTK Guide will continue to be updated regularly based on the new NLP and NLTK updates.

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Owner and Founder at Holistic SEO & Digital

Koray Tuğberk GÜBÜR is the CEO and Founder of Holistic SEO & Digital where he provides SEO Consultancy, Web Development, Data Science, Web Design, and Search Engine Optimization services with strategic leadership for the agency’s SEO Client Projects. Koray Tuğberk GÜBÜR performs SEO A/B Tests regularly to understand the Google, Microsoft Bing, and Yandex like search engines’ algorithms, and internal agenda. Koray uses Data Science to understand the custom click curves and baby search engine algorithms’ decision trees. Tuğberk used many websites for writing different SEO Case Studies. He published more than 10 SEO Case Studies with 20+ websites to explain the search engines. Koray Tuğberk started his SEO Career in 2015 in the casino industry and moved into the white-hat SEO industry. Koray worked with more than 700 companies for their SEO Projects since 2015. Koray used SEO to improve the user experience, and conversion rate along with brand awareness of the online businesses from different verticals such as retail, e-commerce, affiliate, and b2b, or b2c websites. He enjoys examining websites, algorithms, and search engines.

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