How to get Synonyms and Antonyms using Python NLTK

NPL is one of the fastest field in computer science and artificial intelligence . Most of the NLP tasks requires synonyms and antonyms for a particular word . This article will precisely tell you How to get synonyms and antonyms using Python NLTK  . So Lets begin .

Synonyms and Antonyms using NLTK –

Step 1-

Import the require libraries for Synonyms and Antonyms.

import nltk
from nltk.corpus import wordnet

Here NLTK is NLP library in Python which contains a wordnet module. NLTK has so many other functions apart from this .

Step 2-

Creating an empty list for holding Synonyms and Antonyms.

list_synonyms = []
list_antonyms = []

Step 3 –

Here is the code to get the Synonyms for the word “open”. This will append the Synonyms in the list list_synonyms. Let’s see the below code.

for syn in wordnet.synsets("open"): 
    for lemm in syn.lemmas(): 
        list_synonyms.append(lemm.name())

Step 4 –

This code is for finding Antonyms for the same word open. It is also on quite same pattern as Synonyms .

for syn in wordnet.synsets("open"): 
    for lemm in syn.lemmas(): 
        
        if lemm.antonyms(): 
            list_antonyms.append(lemm.antonyms()[0].name())

Output –

Till step 4 we have appended all elements to the corresponding list. Let’s print them. But before printing them we need to convert them into a set .

print(list_synonyms)
print(list_antonyms)

Lets see the overall output before converting the list into set python –

Synonyms and Antonyms using Python NLTK

As we can we so many duplicate values here. Let’s convert the list to set .

 

Synonyms and Antonyms using Python NLTK

As the output shows now we have unique values for Synonyms and Antonyms.

Conclusion –

Well NLTK is really good Natural Language Processing API. NLTK is capable of performing various NLP stuffs like lemmatization, stemmer, POS tagging, etc . It has lots of Natural Language Corpus for programming usages. It is also open source.

I hope you find this article more clear and precise. Keep reading the data science learner’s article.

Thanks 

Data Science Learner Team 

 

 

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