Lemmatizáció

Lemmatizáció is a process in natural language processing and computational linguistics that involves reducing a word to its base or dictionary form, known as its lemma. This is distinct from stemming, which often chops off word endings without regard for the word's actual meaning or grammatical correctness. Lemmatization, on the other hand, uses a vocabulary and morphological analysis to return the canonical form of a word, ensuring it is a valid dictionary word.

For example, the lemma of "running", "ran", and "runs" is "run". Similarly, the lemma of "better" and "best" is "good". This process is crucial for many NLP tasks, such as information retrieval, text analysis, machine translation, and sentiment analysis. By grouping together different inflected forms of a word, lemmatization helps to reduce the size of the vocabulary and improve the accuracy of these applications. It allows systems to treat variations of the same word as equivalent, leading to a more comprehensive understanding of the text.

The effectiveness of lemmatization depends heavily on the quality of the lexicon and the morphological analyzer used. Different languages have different grammatical structures and complexities, requiring specialized lemmatizers for each. For instance, languages with rich inflectional morphology, like Finnish or Turkish, pose greater challenges for lemmatization than languages with less complex morphology, such as English. Libraries and tools like NLTK and spaCy in Python provide lemmatization functionalities for various languages.