What is stemming vs lemmatization?

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Stemming and Lemmatization are both text preprocessing techniques in Natural Language Processing (NLP) that reduce words to their root form, but they work differently in how they process and output results.

🔑 1. Stemming

  • A rule-based, crude approach that chops off prefixes or suffixes to get the root form.

  • It does not always return a meaningful word, just a shortened version.

  • Faster but less accurate.

👉 Example:

  • “playing” → “play”

  • “studies” → “studi”

  • “better” → “bett” (not a valid word)

🔑 2. Lemmatization

  • A dictionary-based, linguistic approach that reduces words to their base or dictionary form (lemma).

  • It considers the context and part of speech before converting.

  • Slower than stemming but more accurate and meaningful.

👉 Example:

  • “playing” → “play”

  • “studies” → “study”

  • “better” → “good” (uses vocabulary knowledge)

⚡ Key Differences

FeatureStemmingLemmatization
MethodRule-based, chops off word endingsDictionary & context-based
OutputMay not be a real wordAlways a valid word (lemma)
SpeedFasterSlower
AccuracyLess accurateMore accurate
Example (“studies”)“studi”“study”

✅ In summary:

  • Stemming is like roughly trimming words to their base, often at the cost of accuracy.

  • Lemmatization is like carefully finding the correct root word using language rules.

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