Analyzers and relevance
On This Lesson 4 topics
This lesson is planned. The outline below lists the topics it will cover. The full lesson has not been published yet.
In this lesson
understand how text is processed and ranked.
Planned topics
- An analyzer = character filters, a tokenizer (split into words), and token filters (lowercase, remove stop words, stemming, synonyms).
- The same analyzer should be applied at index time and query time, unless you deliberately differ (e.g. autocomplete).
- Relevance scoring: BM25 (verify default) rewards rare terms and frequent matches in short fields; field boosts raise importance (title over description).
- Analyzer choice changes results: stemming makes "running" match "run", but can over-match.
- Language matters: use the analyzer for the content language.
Draft outline details
Demonstration
text "Running Shoes!" → tokens running, shoes → with stemming run, shoe.
Check your understanding
What does stemming do?
Show answer
Reduces words to a root so "running" matches "run".
Practice
Apply this lesson with the module's lab, exercises and quiz.
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