What is stemming and lemmatization?
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Stemming and Lemmatization are techniques in Natural Language Processing (NLP) used to reduce words to their base or root form, but they work differently:
1. Stemming
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Definition: Stemming is the process of removing prefixes or suffixes from words to reduce them to a base form (called the “stem”), often by applying simple heuristic rules.
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How it works: It usually cuts off word endings without considering the meaning of the word.
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Example:
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“running” → “run”
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“happily” → “happi”
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“studies” → “studi”
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Pros: Fast and simple.
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Cons: May produce non-dictionary words or inaccurate roots.
2. Lemmatization
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Definition: Lemmatization reduces a word to its dictionary (lemma) form using linguistic knowledge like part of speech and context.
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How it works: It looks at the word’s meaning and grammar to find the correct base word.
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Example:
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“running” → “run”
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“better” → “good”
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“studies” → “study”
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Pros: Produces real words, more accurate than stemming.
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Cons: Slower and more complex because it requires dictionary lookup and POS tagging.
Key Difference
| Feature | Stemming | Lemmatization |
|---|---|---|
| Approach | Heuristic / rule-based | Linguistic / dictionary-based |
| Output | May be non-word (e.g., “happi”) | Real dictionary word (e.g., “happy”) |
| Accuracy | Less accurate | More accurate |
| Performance | Faster | Slower |
| Use case | Quick text preprocessing, search engines | NLP tasks requiring precise meaning |
✅ Summary:
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Stemming is a rough, fast method to reduce words to a root.
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Lemmatization is a more accurate method that reduces words to their proper dictionary form.
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