Lemmasítás

Lemmasítás, or lemmatization, is the process of reducing a word to its lemma, the canonical base form found in dictionaries. The goal is to map inflected or derived forms to a single, linguistically valid base form. Unlike stemming, which often produces truncated or non-dictionary forms, lemmasítás aims for a form that is both meaningful and standardized for linguistic analysis, information retrieval, and corpus studies.

The process typically combines morphological analysis with part-of-speech tagging. A lemmatizer uses a lexicon or morphological rules to determine the correct lemma from the surface form, sometimes requiring disambiguation based on context. Approaches include rule-based systems, dictionary-based lookups, statistical models, and neural networks. Language-specific resources, such as annotated corpora and morphology grammars, are essential for high accuracy, especially in morphologically rich languages.

Applications of lemmasítás span natural language processing pipelines and information retrieval. By normalizing words to their lemmas, systems achieve better matching and comparison: for example, running and run or cars and car are treated as related forms. Lemmatization supports text mining, search engines, machine translation, and linguistic research, enabling more robust analysis across texts with varied inflectional forms.

Challenges include lexical ambiguity, where a form corresponds to multiple lemmas depending on part of speech, irregular or suppletive forms, and the treatment of proper nouns or new coinages. Resource limitations can affect performance in low-resource languages. Evaluation of lemmasítás typically relies on annotated datasets that measure the accuracy of correct lemma assignments across diverse genres.