Tokenizes

Tokenizes is the third-person singular form of the verb tokenize, meaning to convert a sequence of characters or data into discrete units called tokens. In computing and linguistics, tokenization is a fundamental preprocessing step that prepares text or data for further analysis or processing.

In natural language processing, tokenization splits text into tokens such as words, punctuation marks, or subword units. Simple approaches include whitespace tokenization, while more advanced methods apply rules to separate punctuation, handle contractions, and preserve meaningful units. Some tokenizers produce tokens that include punctuation as separate elements, others treat punctuation as separate or ignore it depending on the task.

Subword tokenization has become common in modern NLP. Techniques like Byte-Pair Encoding (BPE), WordPiece, and Unigram models divide words into smaller units, enabling robust handling of rare or unseen words and reducing vocabulary size. Subword tokenization is particularly important for morphologically rich languages and large multilingual models.

In programming languages, tokenization is a stage of lexical analysis performed by a tokenizer or lexer. It converts source code into a stream of tokens such as identifiers, keywords, operators, and literals, which are then consumed by a parser. This process must be deterministic and reproducible for correct compilation or interpretation.

Challenges in tokenization include language diversity, hyphenation rules, punctuation handling, and nonstandard text such as emojis, URLs, or social media slang. Tokenization affects downstream tasks including indexing, search, machine translation, and sentiment analysis, making its design a key consideration in text processing systems.