How computers transform raw text into meaningful symbols
In short: Before a large language model can predict the next word, or a compiler can run your code, a hidden process must first break the chaos of raw characters into organized pieces. This is lexical analysis: the essential first step of turning a stream of symbols into structured information.
At its core, lexical analysis—often performed by a 'lexer' or 'scanner'—is the process of converting raw text into meaningful units called tokens. Unlike the probabilistic tokenization used by modern large language and language models, traditional lexical tokenization is rule-based. It relies on a lexical grammar to categorize strings into specific classes, such as identifiers, operators, or data types. This process typically occurs in two stages: scanning, which segments the input into 'lexemes' (the raw character sequences), and evaluating, which converts those lexemes into processed values for further use.
The complexity of this task varies significantly depending on the language. In programming languages like C, a lexer might identify an assignment operator or a numeric literal. In natural language processing, the lexer identifies parts of speech like nouns and verbs. However, the definition of a 'lexeme' can be tricky; while it often aligns with the linguistic concept of a 'word' in analytic languages like English, it may behave more like a morpheme in highly synthetic or agglutinative languages like Korean.
While many lexers are simple enough to be generated by tools like 'lex' using regular expressions, others are hand-coded for performance or to handle complex features. A lexer's job is strictly limited to identifying individual tokens; it generally lacks the power to understand relationships between them. For instance, a lexer can recognize parentheses as tokens, but it cannot verify if they are correctly matched—that structural responsibility falls to the parser. This division of labor ensures that the initial phase of processing remains efficient and focused on pattern recognition.
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- Summary by Intellect · source: Lexical analysis
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