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The Comprehensive Guide to Tokenization: Concepts, Techniques, and Implementation
1. Introduction
Tokenization is the process of breaking a stream of text into smaller pieces called tokens. These tokens may be words, punctuation marks, numbers, or even subword units, depending on the context and application. Tokenizers serve as the foundational layer for many text processing tasks in both natural language processing (NLP) and compiler design.
In NLP, tokenization is often the first step in preprocessing raw text data before feeding it into algorithms for analysis, sentiment detection, machine translation, or information retrieval. In compiler design, tokenization (or lexical analysis) is used to convert source code into tokens that are then parsed to create an abstract syntax tree (AST).
The significance of tokenization cannot be overstated. Whether you are developing a search engine, building a chatbot, or designing a programming language, the ability to effectively and efficiently break down text into manageable units is essential. In this guide, we will explore tokenization from multiple perspectives and provide detailed Python code examples that illustrate how different tokenizers work.
