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How to Perform Part of Speech Tagging Using the NLTK Library in NLP?

Speech Tagging Using the NLTK Library in NLP

Condition for Performing Part of Speech Tagging Using the NLTK Library in NLP

  • Description:
    In NLP (Natural Language Processing), Part-of-Speech (POS) tagging refers to the task of assigning word classes (such as noun, verb, adjective, etc.) to each word in a sentence.
Step-by-Step Process
  • Import the Necessary Modules:
    You’ll need to import the nltk module and download necessary resources for POS tagging.
  • Tokenize the Text:
    Tokenizing is the process of splitting a sentence into words or tokens.
    You can use nltk.word_tokenize to tokenize your text.
  • Perform POS Tagging:
    Once you have the tokens, you can use nltk.
    pos_tag() to tag the tokens with their respective parts of speech.
  • Explanation of POS Tags:
    Here are some of the common POS tags used in NLTK's averaged_perceptron_tagger:
    'NN': Noun, Singular
    'NNS': Noun, Plural
    'VB': Verb, Base form
    'VBD': Verb, Past tense
    'VBG': Verb, Gerund or present participle
    'JJ': Adjective
    'RB': Adverb
    'DT': Determiner (e.g., "the", "a")
    'IN': Preposition or subordinating conjunction
  • Customizing or Using Different Taggers:
    If you want to use a different POS tagger or a custom-trained tagger, you can switch or fine-tune models based on your specific needs.
    However, for most cases, the default averaged_perceptron_tagger is sufficient.
Sample Code
  • import nltk
    nltk.download('punkt') # for word tokenization
    nltk.download('averaged_perceptron_tagger') # for POS tagging
    from nltk.tokenize import word_tokenize
    text = "NLTK is a great library for NLP."
    tokens = word_tokenize(text)
    print('Tokens :',tokens)
    from nltk import pos_tag
    tagged_tokens = pos_tag(tokens)
    print('Tagged_tokens :',tagged_tokens)
    text = "The quick brown fox jumps over the lazy dog."
    tokens = word_tokenize(text)
    tagged_tokens = pos_tag(tokens)
    for word, tag in tagged_tokens:
      print(f"{word}: {tag}")
Screenshots
  • Speech_tagging