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From "Karl Wettin (JIRA)" <>
Subject [jira] Updated: (LUCENE-626) Adaptive, user query session analyzing spell checker.
Date Sat, 03 Feb 2007 01:47:06 GMT


Karl Wettin updated LUCENE-626:

    Comment: was deleted

> Adaptive, user query session analyzing spell checker.
> -----------------------------------------------------
>                 Key: LUCENE-626
>                 URL:
>             Project: Lucene - Java
>          Issue Type: New Feature
>          Components: Search
>            Reporter: Karl Wettin
>            Priority: Minor
>         Attachments: spellchecker.diff
> From javadocs:
>  This is an adaptive, user query session analyzing spell checker. In plain words, a word
and phrase dictionary that will learn from how users act while searching.
> Be aware, this is a beta version. It is not finished, but yeilds great results if you
have enough user activity, RAM and a faily narrow document corpus. The RAM problem can be
fixed if you implement your own subclass of SpellChecker as the abstract methods of this class
are the CRUD methods. This will most probably change to a strategy class in future version.
> 1. Gram up results to detect compositewords that should not be composite words, and vice
> 2. Train a gramed token (markov) chain with output from an expectation maximization algorithm
(weka clusters?) parallel to a closest path (A* or bredth first?) to allow contextual suggestions
on queries that never was placed.
> Usage:
> Training
> At user query time, create an instance of QueryResults containg the query string, number
of hits and a time stamp. Add it to a chronologically ordered list in the user session (LinkedList
makes sense) that you pass on to train(sessionQueries) as the session times out.
> You also want to call the bootstrap() method every 100000 queries or so.
> Spell checking
> Call getSuggestions(query) and look at the results. Don't modify it! This method call
will be hidden in a facade in future version.
> Note that the spell checker is case sensitive, so you want to clean up query the same
way when you train as when you request the suggestions.
> I recommend something like query = query.toLowerCase().replaceAll(" ", " ").trim() 

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