Tokenizál

Tokenizál is a Hungarian verb that translates to "tokenize" in English. It is a fundamental process in natural language processing (NLP) and computer science. In essence, tokenization involves breaking down a sequence of text into smaller units called tokens. These tokens can be words, punctuation marks, numbers, or even sub-word units depending on the specific application.

The primary purpose of tokenization is to prepare text data for further analysis or processing. Computers do not inherently understand human language in its raw form. By tokenizing text, we transform it into a structured format that algorithms can more easily work with. For example, when analyzing sentiment in a sentence, each word or token is treated as an individual feature that can be assigned a sentiment score.

Different tokenization strategies exist. Word tokenization is the most common, where text is split based on spaces and punctuation. However, for languages like Hungarian, which are highly agglutinative (meaning words are formed by adding many suffixes), sub-word tokenization or morphological analysis might be more appropriate. This allows for a more granular understanding of word structure and meaning. Tokenization is a crucial preprocessing step for a wide range of NLP tasks, including machine translation, text classification, and information retrieval.