#### Lemmatization

Lemmatization reduces words to their base or dictionary form (lemma). For example, "running", "ran", and "runs" all become "run". This groups related word forms together for more meaningful analysis.

#### Comparison

- Lemmatization: Uses linguistic knowledge to produce valid dictionary words (studies → study)
- Stemming: Uses simple rules to chop word endings (studies → studi)
- Advantage: Lemmatization produces readable, meaningful base forms

#### Usage

Apply lemmatization after tokenization to consolidate word variants. Particularly useful for topic modeling and keyword extraction where grouping related forms improves interpretability.

Learn More: spaCy Lemmatization Guide