lemmatizáló

A lemmatizáló, or lemmatizer, is a natural language processing component that reduces inflected or derived word forms to their lemma, the canonical base form found in a dictionary. Lemmas serve as standardized representations of words for tasks such as indexing, search, and linguistic analysis.

Most lemmatizers combine morphological analysis with dictionaries or rules. They may be rule-based, rely on statistical models, or use neural networks. Language-specific resources are usually required because morphology varies greatly between languages, especially in morphologically rich ones.

Lemmatization differs from stemming. A lemmatizer aims to produce a valid base form that exists in the language’s lexicon, whereas stemming simply trims affixes and may produce non-lexical remnants. Some systems integrate lemmatization with part-of-speech tagging to improve accuracy, since the correct lemma often depends on the word’s grammatical role.

Typical workflow includes tokenization, part-of-speech tagging, disambiguation using context, and mapping surface forms to lemmas through a lexicon and rules or a learned model. In many pipelines, lemmatization is coupled with other components such as morphological analyzers, sentence segmentation, or dependency parsers.

Applications span information retrieval, text mining, machine translation, and various NLP tasks that benefit from normalized word forms. Challenges include resolving ambiguity among multiple possible lemmas, handling proper names, language-specific morphophonemic changes, and adapting resources to domain or low-resource languages. Evaluation generally relies on gold-standard lemmas and measures of accuracy or F1 against annotated corpora.