Tokentípusú

Tokentípusú is a Hungarian term that refers to the classification of tokens in computational linguistics, particularly in natural language processing (NLP). Tokens are the smallest units of text that can be analyzed individually, such as words, punctuation marks, or symbols. The concept of token types helps categorize these units based on their linguistic or functional roles.

In Hungarian NLP, token types are often distinguished based on their syntactic or semantic properties. Common token types include:

- **Words**: Lexical units that carry meaning, such as nouns, verbs, adjectives, and adverbs.

- **Punctuation**: Symbols like commas, periods, or question marks that structure sentences.

- **Symbols**: Special characters like numbers, hashtags, or emojis, which may carry specific meanings in context.

- **Whitespace**: Spaces or line breaks that separate tokens but do not carry semantic value.

Tokenization, the process of breaking text into tokens, is crucial for tasks like text preprocessing, machine translation, and sentiment analysis. Different languages, including Hungarian, may require specialized tokenization rules due to unique morphological features, such as agglutinative word forms or complex punctuation usage.

In Hungarian, tokenization can be particularly challenging due to the language’s rich inflectional morphology, where a single word may represent multiple grammatical forms. Tools like the Stanford CoreNLP or language-specific libraries (e.g., HunPos) are often used to accurately tokenize and classify tokens in Hungarian text. Proper token typing ensures that downstream NLP tasks, such as part-of-speech tagging or named entity recognition, function effectively.