To perform Parts of speech identification and Synonym using Natural Language Processing (NLP) techniques. </H3>
Step 1: Import the nltk library.
Step 2: Download the 'punkt', 'wordnet', and 'averaged_perceptron_tagger' resources.
Step 3:Accept user input for the text.
Step 4:Tokenize the input text into words using the word_tokenize function.
Step 5:Iterate through each word in the tokenized text.
• Perform part-of-speech tagging on the tokenized words using nltk.pos_tag.
• Print each word along with its corresponding part-of-speech tag.
• For each verb , iterate through its synsets (sets of synonyms) using wordnet.synsets(word).
• Extract synonyms and antonyms using lemma.name() and lemma.antonyms()[0].name() respectively.
• Print the unique sets of synonyms and antonyms.
!pip install nltk
import nltk
#import wordnet
nltk.download( 'punkt' )
nltk.download('wordnet')
from nltk.tokenize import word_tokenize
nltk.download( 'averaged_perceptron_tagger' )
sentence=input()
# Tokenize the sentence into words
words = word_tokenize(sentence)
# Identify the parts of speech for each word
pos_tags= nltk.pos_tag(words)
# Print the parts of speech
for word, tag in pos_tags:
print(word, tag)
from nltk.corpus import wordnet
# Identify synonyms and antonyms for each word
synonyms =[]
antonyms =[]
for word in words:
for syn in wordnet.synsets(word) :
for lemma in syn.lemmas():
synonyms . append (lemma . name( ) )
if lemma . antonyms():
antonyms . append ( lemma. antonyms ( ) [0] . name ( ) )
# Print the synonyms and antonyms
print ( "Synonyms : " ,set (synonyms) )
print ( "Antonyms : " ,set(antonyms) )
i.) Sample Input
ii.) Sample Output
Thus ,the program to perform the Parts of Speech identification and Synonymis executed sucessfully.
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