What is lemmatization?

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Lemmatization is a text preprocessing technique in Natural Language Processing (NLP) that reduces a word to its base or dictionary form (lemma) while keeping the meaning intact.

🔹 How it works

  • Lemmatization uses morphological analysis + vocabulary + grammar rules.

  • It considers the context (part of speech: noun, verb, adjective, etc.).

  • Example:

    • “running” → “run” (verb form)

    • “better” → “good” (adjective form)

    • “mice” → “mouse” (noun form)

🔹 Difference from Stemming

  • Stemming: Cuts off word endings without context → may produce invalid words.

    • “studies” → “studi”

  • Lemmatization: Returns proper dictionary form → linguistically correct.

    • “studies” → “study”

🔹 Why it’s important

  • Reduces word variations → improves search engines, chatbots, sentiment analysis, and machine learning models.

  • Helps models treat related words as the same concept.

✅ In short:
Lemmatization maps inflected or derived words to their root dictionary form using grammar and context, making NLP tasks more accurate than simple stemming.

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