Stemming and Lemmatization are text normalization techniques in Natural Language Processing (NLP) that reduce words to their base forms, but they differ in their approach: stemming is a rule-based, fast, and potentially inaccurate method, while lemmatization is context-aware, dictionary-based, and more accurate but slower.
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Stemming
- Definition: A heuristic process that removes suffixes or prefixes from words to obtain their base or stem form.
- Method: Uses a set of rules to strip off common word endings.
- Example: “running,” “runner,” and “runs” could all be stemmed to “run”.
- Pros:
- Speed: Faster than lemmatization due to its rule-based nature.
- Simplicity: Easier to implement.
- Cons:
- Accuracy: May produce non-dictionary words as stems.
- Context: Doesn’t consider the context of the word.
Lemmatization
- Definition: A process that uses a lexicon (dictionary) and morphological analysis to reduce words to their lemma (dictionary form).
- Method: Considers the word’s context and part-of-speech to find the correct base form.
- Example: “better” could be lemmatized to “good”.
- Pros:
- Accuracy: More accurate than stemming because it uses context and dictionary lookups.
- Meaningful Output: Produces real words (lemmas).
- Cons:
- Speed: Slower than stemming due to its context-aware and dictionary-based approach.
- Complexity: More complex to implement.
Key Differences Summarized
| Feature | Stemming | Lemmatization |
| Method | Rule-based | Context and dictionary-based |
| Output | May produce non-dictionary words | Produces real words (lemmas) |
| Accuracy | Lower | Higher |
| Speed | Faster | Slower |
| Context | Doesn’t consider context | Considers context |
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