Steps in NFA-based tokenization
Step 1 – Convert the regular expression into an equivalent NFA: This conversion involves representing the regex as a state machine with epsilon transitions and non-deterministic choices.
Step 2 – Simulate the NFA: Traverse the NFA based on the input text, exploring all possible transitions simultaneously.
Step 3 – Track possible token matches: Maintain a set of current states representing all possible matches at any point in the input text. Emit tokens when reaching accepting states.
How DFA and NFA help for Tokenization of “Regular Expression”.
Regular expressions (regex) are the universal tools for data pattern matching and processing text. In a widespread way, they are used in different programming languages, various text editors, and even software applications. Tokenization, the process that involves breaking down the text into smaller pieces called features using the tokens, plays a role in many language processing tasks, including word analysis, parsing, and data extraction. The idea of Deterministic Finite Automata (DFA) and Non-deterministic Finite Automata (NFA) is fundamental in computer science, among other things, because of defines the grammar rules of regular expressions (regex). This article details how DFA and NFA simplify the tokenization of regular expressions.
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