lemmatizaci

Lemmatizace, or lemmatization, is the process of reducing a word to its lemma, the canonical dictionary form. In languages with rich morphology, a single lemma can appear in many inflected forms, such as different case endings, numbers, or tenses. In Czech, the noun lemma lemmatizace may appear in various inflected forms; for example, lemmatizaci is the accusative singular form of lemmatizace. Lemmatization aims to map every surface form to its base lemma so that related word forms are treated as the same item in processing and analysis.

Lemmatization differs from stemming. Stemming removes affixes to reduce a word to a rough base form, which may not be a valid word. Lemmatization, by contrast, uses linguistic knowledge, including morphology and part-of-speech information, to produce a legitimate lemma that can be found in a dictionary. This requires language-specific resources such as morphological analyzers, dictionaries, and linguistic rules.

Approaches to lemmatization range from rule-based and dictionary-driven methods to statistical and neural models. Classical systems rely on hand-crafted rules and lexicons, while modern pipelines may combine morphological analysis with machine learning to disambiguate lemmas based on context. For languages with complex morphology, high-quality lexica and accurate part-of-speech tagging greatly improve performance.

Applications of lemmatization include information retrieval, text normalization, machine translation, text mining, and linguistic research. It helps reduce data sparsity, improve search recall, and support downstream NLP tasks by providing consistent lemma-level representations. Challenges include handling polysemy, multiword expressions, proper nouns, and out-of-vocabulary forms, especially in low-resource languages.