This document discusses using the Natural Language Toolkit (NLTK) in Python to find synonyms and antonyms of words. It describes how WordNet contains words grouped by their relationships and meanings. It provides code to import NLTK and WordNet, declare arrays to store synonyms and antonyms, retrieve synonyms using a for loop through WordNet synsets, append the synonym names to the synonyms array, check for antonyms using the lemmas' antonyms method and append to the antonyms array, and finally print the arrays.

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Finding Antonyms And
Synonym
Using
Python Natural Language
Processing Toolkit

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Synonyms/Antonyms Using Python NLTK
Wordnet is a large collection of words and vocabulary from the English language that
are related to each other and are grouped in some way. A collection of similar words is
called lemmas.
import nltk
from nltk.corpus import wordnet
Step#1 Import NLTK and Wordnet

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Synonyms/Antonyms Using Python NLTK
array_synonyms = []
array_antonyms = []
Step#2 Declare 2 arrays to collect Synonyms and Antonyms
for vsyn in wordnet.synsets("good"):
Step#3 Retrieve all synonym using loop
Synsets: synonyms are words that have similar meanings. A synonym set, or synset, is
a group of synonyms. A synset, therefore, corresponds to an abstract concept.
Step#4 Retrieve synonym names (using lemmas name property)
And append it to array
for l in vsyn.lemmas():
print(l.name())
array_synonyms.append(l.name())
The synonyms contained within a synset are called lemmas. You can access the string versions
of these synonyms via a Synset's lemma_names property

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Synonyms/Antonyms Using Python NLTK
Step#5 Check Lemmas Antonyms method
And append it to array_antonyms array
if l.antonyms():
array_antonyms.append(l.antonyms()[0].name())
Step#6 Print Array output
print(array_synonyms)
print(array_antonyms)

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