From text to token: How tokenization pipelines work
2 days ago
- #tokenization
- #text-processing
- #search-engines
- Tokenization transforms text into searchable tokens by breaking down input text into normalized, consistent units.
- The process includes filtering (lowercasing, removing diacritics), tokenization (splitting text into words or parts), stopword removal, and stemming (reducing words to their root forms).
- Different tokenizers exist for various needs: word-oriented for whole words, partial word for fragments (like n-grams), and structured text for specific formats (URLs, emails).
- Stopwords (common words like 'the') are often removed to focus on meaningful terms, though this is configurable based on context.
- Stemming simplifies words to their base forms (e.g., 'jumping' → 'jump'), improving search consistency despite sometimes odd-looking results.
- Tokenization is foundational for search engines, ensuring queries match indexed content accurately, even with variations in word forms or punctuation.
- Modern search databases (e.g., Elasticsearch, Tantivy, Postgres) offer customizable tokenization pipelines to suit different search requirements.