lemmatize

Lemmatization is the process of reducing a word to its lemma, the canonical base form found in a dictionary. It uses vocabulary and morphological analysis to determine the appropriate dictionary form, and it often requires the word’s part of speech to select the correct lemma. This makes lemmatization more linguistically informed than simple stemming, which relies on heuristic affix stripping.

In practice, a lemmatizer consults a lexical resource that lists lemma forms for different inflections and may apply rules for irregular forms. With the word and its POS, the algorithm returns the lemma. For example, running as a verb yields run; went as a past tense verb yields go; better as an adjective can yield good.

Lemmatization differs from stemming in that it aims to produce valid dictionary forms rather than arbitrary or truncated stems. Stemmers may produce nonwords or misleading forms, whereas lemmatizers seek a linguistically correct base form based on language rules and lexicons.

Applications of lemmatization include information retrieval, text preprocessing for natural language processing, and any task that benefits from normalizing word forms. It is language-dependent and relies on quality lexicons and POS tagging. In practice, lemmatization improves matching and analysis by treating inflected or derived forms of a word as a single item, aiding downstream tasks such as indexing, clustering, and semantic understanding.