tokenize

Tokenize is the process of converting a sequence of input data into tokens, which are discrete units that carry meaning for subsequent processing. A token can be a word, a number, a punctuation mark, or a programming language keyword or symbol, depending on the context. Tokenization is a common preprocessing step in natural language processing, data parsing, and compiler design because it simplifies the structure of the input and defines the boundaries for analysis.

In natural language processing, tokenization splits text into tokens and is influenced by language, orthography, and encoding. Word-level tokenization divides text into words, while subword or byte-pair encoding tokenization breaks words into smaller units to handle rare or unknown words. Character-level tokenization treats each character as a token. Challenges include handling contractions, punctuation, whitespace, and scripts without explicit word boundaries.

In programming languages, tokenization (lexical analysis) converts source code into tokens such as identifiers, keywords, literals, and operators. A lexer or scanner performs this task and feeds tokens to a parser. Tokenization must handle escape sequences, numeric formats, and comments, and may be influenced by the language's grammar and context.

In data security, tokenization replaces sensitive data with non-sensitive tokens stored in a secure vault. The mapping is reversible only by the tokenization system, allowing systems to operate on tokens without exposing underlying data. Tokenization is used to reduce exposure of credit card numbers and other sensitive identifiers, often as part of PCI DSS compliance.

Tokenization is distinct from encryption or hashing: tokens do not reveal the original data on their own, and reversible mapping is required for legitimate use. It may be combined with other privacy-preserving techniques or managed by specialized tokenization services or libraries.