Explain tokenization in NLP.

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Tokenization is one of the fundamental steps in Natural Language Processing (NLP). It is the process of breaking down text into smaller units called tokens, which can be words, phrases, or even characters, depending on the application. Tokens are the basic building blocks that NLP models use to understand and process language.

How Tokenization Works

  1. Word Tokenization:

    • Splits text into individual words or terms.

    • Example:
      "I love machine learning" → ["I", "love", "machine", "learning"]

  2. Sentence Tokenization:

    • Divides text into sentences.

    • Example:
      "I love NLP. It is very useful." → ["I love NLP.", "It is very useful."]

  3. Subword or Character Tokenization:

    • Splits text into smaller units like subwords or characters, useful for handling unknown words or languages with complex morphology.

    • Example: "learning" → ["learn", "##ing"] (used in models like BERT)

Why Tokenization is Important

  • Preprocessing: Converts raw text into manageable pieces for further analysis.

  • Feature Extraction: Tokens are used to create features for machine learning models, such as word embeddings.

  • Handling Complexity: Helps models understand context, grammar, and meaning by working at the token level.

Challenges in Tokenization

  • Punctuation & Special Characters: Deciding whether to treat them as separate tokens.

  • Compound Words: Some languages merge words, which requires careful handling.

  • Ambiguity: Words can have multiple meanings depending on context.

Summary

Tokenization is the process of splitting text into meaningful units, enabling NLP systems to analyze, process, and understand human language effectively. It’s the first step in almost every NLP pipeline, from sentiment analysis to machine translation.

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