ApexDelight

Stemming vs Lemmatization: Which is Better?

• apexdelight
• March 20, 2025March 20, 2025
• 0

Stemming and lemmatization are both techniques used in Natural Language Processing (NLP) to reduce words to their base form, but they differ in approach and accuracy.

1. Stemming

• Definition: Stemming reduces a word to its root form by chopping off suffixes, often without considering the meaning.
• Approach: Uses simple rules or heuristics (e.g., removing common suffixes like -ing, -ed, -ly).
• Example:
  • running → run
  • better → bet
  • studies → studi
• Pros:
  • Fast and computationally efficient.
• Cons:
  • Can produce non-real words (e.g., “happi” instead of “happy”).
  • Less accurate due to over-stemming (removing too much) or under-stemming (not removing enough).

2. Lemmatization

• Definition: Lemmatization reduces a word to its dictionary form (lemma) while considering context and meaning.
• Approach: Uses linguistic knowledge (e.g., WordNet) to return the correct base form.
• Example:
  • running → run
  • better → good
  • studies → study
• Pros:
  • More accurate and produces real words.
• Cons:
  • Slower than stemming because it requires morphological analysis (e.g., checking part of speech).

Key Differences

Feature	Stemming	Lemmatization
Method	Rule-based, removes suffixes	Dictionary-based, considers meaning
Speed	Faster	Slower
Accuracy	Lower (can create non-words)	Higher (produces real words)
Example	Caring → Car	Caring → Care

When to Use What?

• Use Stemming when speed is more important than accuracy (e.g., quick text indexing).
• Use Lemmatization when accuracy is important (e.g., NLP tasks like chatbots, search engines, or sentiment analysis).

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