Aim: to perform Parts of speech identification and Synonym using Natural Language Processing (NLP) techniques.
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.
import nltk
from nltk.corpus import wordnet
nltk.download('punkt')
nltk.download('averaged_perceptron_tagger')
nltk.download('wordnet')
def get_synonyms(word):
synonyms = set()
for syn in wordnet.synsets(word):
for lemma in syn.lemmas():
synonyms.add(lemma.name())
return synonyms
def process_text_file(file_path):
with open(file_path, 'r') as file:
text = file.read()
return text # Return the processed text
text = process_text_file('sample.txt')
# Tokenize the text into sentences
sentences = nltk.sent_tokenize(text)
for sentence in sentences:
# Tokenize each sentence into words
words = nltk.word_tokenize(sentence)
# Perform part-of-speech tagging
pos_tags = nltk.pos_tag(words)
# Extract verbs
verbs = [word for word, pos in pos_tags if pos.startswith('V')]
# Get synonyms for each verb
for verb in verbs:
synonyms = get_synonyms(verb)
print(f"Verb: {verb}")
print(f"Synonyms: {', '.join(synonyms)}\n")
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