Tokenization is the process of breaking down text into smaller units, called tokens. These tokens can be words, subwords, or even individual characters, depending on the specific approach and the language involved. Tokenization is a fundamental step in natural language processing (NLP) because it helps machines understand and work with human language by converting raw text into a structured format. Proper tokenization ensures that important linguistic features are preserved and that subsequent tasks—such as part-of-speech tagging, parsing, or language modeling—are more accurate and efficient.
How It Works:
- Splitting by Delimiters: In its simplest form, tokenization splits text at spaces and punctuation.
- Language-Specific Rules: More advanced tokenization takes into account linguistic rules, handling contractions, compound words, or special scripts more accurately.
- Subword Tokenization: Modern NLP models often use subword-level tokenization (e.g., Byte-Pair Encoding, WordPiece) to handle rare words and rich morphological variations more effectively.
Why It Matters:
Tokenization is the bridge between raw text and machine-readable data structures. Without effective tokenization, downstream NLP tasks may be less accurate or more computationally expensive. By carefully segmenting text into meaningful units, tokenization sets the stage for more robust language understanding and enables models to handle diverse languages and writing systems with greater flexibility.
