tokenizing

Tokenizing, or tokenization, is the process of converting a sequence of characters into smaller units called tokens. Tokens are the basic units for analysis or processing and can be words, subwords, punctuation marks, or other meaningful elements, depending on the context. The term is used in linguistics, natural language processing, software engineering, and data security.

In natural language processing, tokenization is a preprocessing step that splits text into tokens for subsequent tasks such as parsing, tagging, or vectorization. Approaches include word-level tokenization, which uses whitespace and punctuation to separate tokens; character-level tokenization, which treats individual characters as tokens; and subword tokenization, using methods like Byte Pair Encoding, WordPiece, or SentencePiece to balance vocabulary size with representation of rare or unseen words. Tokenizers must handle language-specific features, contractions, hyphenation, and, for some languages, scripts without explicit word boundaries. After tokenization, further normalization may occur, such as lowercasing, removing stop words, or lemmatization.

In software engineering, tokenization is the first stage of lexical analysis in compilers and interpreters. Source code is divided into tokens such as keywords, identifiers, literals, operators, and punctuation. The tokenizer applies the language’s lexical rules and then the parser builds a syntax tree from the token stream. It must typically ignore or preserve comments and string literals as appropriate.

In data security, tokenization replaces sensitive data with non-sensitive tokens that map to the original data in a secure vault. The mapping is stored separately and access-controlled. Tokenization reduces exposure of sensitive information and supports compliance with privacy and security standards in fields like payment processing and healthcare. Unlike encryption, tokenized values do not reveal the original data without access to the tokenization system, and tokens are designed to be non-reversible without that system.

Implementation considerations include token granularity, reversibility, performance, and compatibility with downstream processing. Many NLP libraries provide ready-to-use tokenizers; specialized tokenization tools are used in data protection and PCI-compliant environments.