Tokenization - Text Processing

Introduction

Tokenization is the foundational step in any Natural Language Processing pipeline. It is the process of breaking raw text into individual meaningful units called tokens. These tokens might be words, subwords, characters, or sentences depending on the tokenization strategy. Every NLP model, from simple bag-of-words classifiers to sophisticated transformers like GPT and BERT, begins by tokenizing input text. The quality of tokenization directly affects downstream task performance.

Why Tokenization Matters

Raw text is just a sequence of characters. Computers need structured input. Tokenization creates that structure by identifying boundaries between meaningful units. Consider the sentence: "I can't believe it's raining!" A naive split on spaces gives ["I", "can't", "believe", "it's", "raining!"] but a good tokenizer might produce ["I", "ca", "n't", "believe", "it", "'s", "raining", "!"] preserving linguistic meaning (contractions split into components).

Types of Tokenization

Word-Level Tokenization

Subword Tokenization (BPE - Byte Pair Encoding)

WordPiece (used by BERT)

SentencePiece (used by T5, LLaMA)

Tokenization in Practice

Handling Special Cases

Impact on Model Performance

Summary

Tokenization bridges the gap between human-readable text and machine-processable numbers. Modern subword tokenization (BPE, WordPiece, SentencePiece) elegantly solves the open-vocabulary problem by decomposing rare words into frequent subword units while keeping common words as single tokens. Understanding tokenization is essential because it directly affects model vocabulary size, sequence length, computational cost, and the ability to handle diverse languages and domains.