What is tokenization in NLP?

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Tokenization in Natural Language Processing (NLP) is the process of breaking down text into smaller units called tokens. Tokens can be words, subwords, characters, or even sentences, depending on the application. It is usually the first step in text preprocessing, as most NLP models cannot directly work on raw text.

Example:
Input: "NLP makes machines understand language."
Word-level tokens → [NLP, makes, machines, understand, language, .]
Character-level tokens → [N, L, P, m, a, k, e, s, ...]

Types of Tokenization:

  1. Word Tokenization – Splitting by words (common in sentiment analysis, text classification).

  2. Subword Tokenization – Splits words into smaller meaningful units (used in BERT, GPT). E.g., “unhappiness” → [“un”, “happiness”].

  3. Sentence Tokenization – Splitting text into sentences (used in summarization, translation).

  4. Character Tokenization – Each character is a token (useful for languages like Chinese or handling misspellings).

Applications:

  • Text preprocessing for ML models.

  • Search engines (indexing words).

  • Chatbots & machine translation.

  • Sentiment and topic analysis.

👉 In short, tokenization converts unstructured text into structured, analyzable units, forming the foundation for almost all NLP tasks.

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