Understanding Tokenization Pipelines: How Search Engines Transform Text into Searchable Tokens
By
philippemnoel
Sesame, salt, and substance. A flagship bake.
Summary
This article explains how search engines process text through tokenization pipelines, breaking down the technical process of transforming raw text into searchable tokens. It covers the key steps including character filtering, tokenization, stemming, and stopword removal, detailing how search engines dismantle input text, clean it, and reassemble it into abstract tokens that power inverted indexes for efficient searching.
Key quotes
· 3 pulledThey dismantle input text (both indexed and query), scrub it clean, and reassemble it into something slightly more abstract and far more useful: tokens.
These tokens are what you search with, and what is stored in your inverted indexes to search over.
When you type a sentence into a search box, it's easy to imagine the search engine seeing the same thing you do. In reality, search engines (or search databases) don't store blobs of text, and they don't store sentences.
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