Introduction
Before any comparison happens, text must be broken down into manageable pieces. That’s where tokenization comes in. It’s the unsung hero of text comparison—turning raw text into structured data that algorithms can analyze.
What Is Tokenization?
Definition: The process of splitting text into smaller units called tokens—usually words, characters, or subwords.
Why it matters: Algorithms like LCS and Levenshtein operate on tokens. Without tokenization, they wouldn’t know where to begin.
Types of Tokenization
- Word Tokenization: Splits text by spaces and punctuation. → “Text comparison is useful” →
["Text", "comparison", "is", "useful"] - Character Tokenization: Breaks text into individual letters. → “Text” →
["T", "e", "x", "t"] - Subword Tokenization: Handles compound or rare words. → “Unhappiness” →
["un", "happi", "ness"] - Sentence Tokenization: Splits paragraphs into sentences. → “This is a sentence. Here’s another.” →
["This is a sentence.", "Here’s another."]
Why Tokenization Matters in Comparison
- Ensures consistent input for algorithms
- Improves accuracy in detecting changes
- Handles edge cases like punctuation, contractions, and formatting
- Enables multilingual support and semantic analysis
To Summarize
Tokenization is the first—and arguably most important—step in text comparison. It turns messy, human language into clean, analyzable data. Whether you’re comparing essays, contracts, or code, tokenization lays the groundwork for meaningful insights.