Text Tokenization with Python

Introduction

In this project, you will learn how to implement a text tokenization system using Python. Text tokenization is a fundamental task in natural language processing, where a given text is broken down into smaller units called tokens. These tokens can represent words, numbers, punctuation, or other meaningful elements in the text. The ability to tokenize text is essential for many applications, such as lexical analysis in compilers, sentiment analysis, and text classification.

👀 Preview

## text = 'total = 1 + 2 * 3'

tokens = [Token(type='NAME', value='total'), Token(type='WS', value=' '), Token(type='EQ', value='='), Token(type='WS', value=' '), Token(type='NUM', value='1'), Token(type='WS', value=' '), Token(type='ADD', value='+'), Token(type='WS', value=' '), Token(type='NUM', value='2'), Token(type='WS', value=' '), Token(type='MUL', value='*'), Token(type='WS', value=' '), Token(type='NUM', value='3')]

🎯 Tasks

In this project, you will learn:

• How to define a Token class to represent the tokens in the text
• How to implement a generate_tokens function that takes an input text and generates a stream of tokens
• How to test the tokenization process with a sample text

🏆 Achievements

After completing this project, you will be able to:

• Understand the concept of text tokenization and its importance in natural language processing
• Implement a basic text tokenization system using Python
• Customize the tokenization process by defining different token types and their corresponding regular expressions
• Test and debug the tokenization system with various input texts