What is Tokenizing?
Tokenization is the process of breaking text into smaller units—typically words, phrases, or subwords. Search engines use tokens to build indexes and match queries.
How It Works (quick)
- Basic: Split on whitespace/punctuation.
- Advanced: Handle compound words, diacritics, morphology, and scripts.
- Subword tokenization: Splits words into smaller units for ML models.
- Normalization: Lowercasing, stemming/lemmatization applied after tokenization.
Why It Matters in E-commerce
- Converts messy queries into structured pieces.
- Handles multi-language inputs (e.g., German compounds, accented French terms).
- Ensures accurate search recall and highlighting.
Best Practices
- Use language-specific analyzers.
- Normalize consistently (case, diacritics).
- Avoid over-splitting brand names or SKUs.
- Tune for short, telegraphic queries in search.
Summary
Tokenizing is the foundation of text search. Good tokenization improves recall, highlighting, and overall user experience.
