From Text to Token: How Tokenization Pipelines Work (www.paradedb.com)

This explainer walks through how search systems convert raw text into the smaller, normalized units called tokens that power indexing and queries. Using the sentence "The full-text database jumped over the lazy café dog," it shows the typical pipeline: character normalization (lowercasing and diacritic folding so café → cafe), tokenization (splitting into units using whitespace/punctuation or specialized tokenizers), optional stopword removal ("the", "and"), and stemming (reducing jumped → jump, lazy → lazi, database → databas). It also categorizes tokenizers into word-oriented, partial-word (n-gram/edge n-gram useful for autocomplete/fuzzy matching), and structured-text tokenizers for URLs/emails, and notes exceptions like code search where casing/symbols must be preserved. The significance is practical: tokenization decisions determine what matches and how relevance is computed. Choices like enabling stopword lists, using stemming vs lemmatization, or choosing n-grams affect precision, recall, and noise (e.g., overstemming can conflate university and universe). Ranking models like BM25 can reduce the need for stopword removal, while vector search offers a semantic alternative to strict lexical pipelines. Good tokenization keeps indexed and query pipelines aligned, preserves positional info for proximity queries, and underpins everything from search relevance to autocomplete.