如何修改NLTK分词代码以按词性分类输出结果？

Step-by-Step Implementation #

• First, ensure you've downloaded required NLTK resources (run once):

  import nltk
  nltk.download('punkt')
  nltk.download('averaged_perceptron_tagger')
  

• Use NLTK's word_tokenize to split text into words, then pos_tag to get part-of-speech tags.
• Filter tags to separate nouns and verbs:
  • Nouns correspond to tags starting with NN (e.g., NN, NNS, NNP, NNPS)
  • Verbs correspond to tags starting with VB (e.g., VB, VBD, VBG, VBN, VBP, VBZ)
• Format results into the requested list syntax.

Complete Code Example #

import nltk
from nltk.tokenize import word_tokenize
from nltk.tag import pos_tag

# Download required resources (run once)
nltk.download('punkt')
nltk.download('averaged_perceptron_tagger')

def categorize_pos(text):
    # Tokenize and tag input text
    tokens = word_tokenize(text)
    tagged_tokens = pos_tag(tokens)
    
    # Separate nouns and verbs based on POS tags
    nouns = [word for word, tag in tagged_tokens if tag.startswith('NN')]
    verbs = [word for word, tag in tagged_tokens if tag.startswith('VB')]
    
    # Output in the requested format
    print(f'nouns = {nouns}')
    print(f'verbs = {verbs}')

# Test with sample text
sample_text = "Natural language processing is fascinating"
categorize_pos(sample_text)

Example Output #

nouns = ['Natural', 'language', 'processing']
verbs = ['is', 'fascinating']

Customization Notes #

• Adjust tag filters if you need precise control (e.g., exclude proper nouns using tag in ['NN', 'NNS'] instead of startswith('NN')).
• The output directly uses valid Python list syntax as requested.

内容的提问来源于stack exchange，提问作者Ali