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https://issues.apache.org/jira/browse/LUCENE-626?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Karl Wettin updated LUCENE-626:
-------------------------------
Description:
Some minor changes to how the single token ngram spell checker in
contrib/spellcheck, but nothing that breaks any old implementation I think.
Also fixed the broken test.
NgramPhraseSuggestier tokenizes a query and suggests combinations of the single
token suggestions matrix.
They must match as a query against an apriori index. By using a span near query
(default) you get features like this:
assertEquals("lost in translation", ngramSuggester.didYouMean("lost on
translation"));
If term position vectors are available it is possible to make it context
sensitive (or what one may call it) to suggest a new term order.
assertEquals("heroes might magic", ngramSuggester.didYouMean("magic light
heros"));
assertEquals("heroes of might and magic", ngramSuggester.didYouMean("heros
on light and magik"));
assertEquals("best game made", ngramSuggester.didYouMean("game best made"));
assertEquals("game made", ngramSuggester.didYouMean("made game"));
assertEquals("game made", ngramSuggester.didYouMean("made lame"));
assertEquals("the game", ngramSuggester.didYouMean("the game"));
assertEquals("in the fame", ngramSuggester.didYouMean("in the game"));
assertEquals("game", ngramSuggester.didYouMean("same"));
assertEquals(0, ngramSuggester.suggest("may game").size());
SessionAnalyzedDictionary is the adaptive layer, that learns from how users
changed their queries, what data they inspected, et c. It will automagically
find and suggest synonyms, decomposed words, and probably a lot of other neat
features I still have not detected.
A bit depending on the situation, ignored suggestions get suppresed and
followed suggestions get suggeted even more.
assertEquals("the da vinci code", dictionary.didYouMean("thedavincicode"));
assertEquals("the da vinci code", dictionary.didYouMean("the davinci
code"));
assertEquals("homm", dictionary.didYouMean("heroes of might and magic"));
assertEquals("heroes of might and magic", dictionary.didYouMean("homm"));
assertEquals("heroes of might and magic 2", dictionary.didYouMean("heroes
of might and magic ii"));
assertEquals("heroes of might and magic ii", dictionary.didYouMean("heroes
of might and magic 2"));
The adaptive layer is not yet(tm) persistent, but soft referenced so that the
dictionary don't go eat up all your RAM.
was:
>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.
TODO:
1. Gram up results to detect compositewords that should not be composite words,
and vice verse.
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()
Lucene Fields: [Patch Available]
Assignee: Karl Wettin
Issue Type: Improvement (was: New Feature)
Summary: Extended spell checker with phrase support and adaptive user
session analysis. (was: Adaptive, user query session analyzing spell checker.)
All of the old comments was obsolete, so I re-initialized the whole issue.
> Extended spell checker with phrase support and adaptive user session analysis.
> ------------------------------------------------------------------------------
>
> Key: LUCENE-626
> URL: https://issues.apache.org/jira/browse/LUCENE-626
> Project: Lucene - Java
> Issue Type: Improvement
> Components: Search
> Reporter: Karl Wettin
> Assigned To: Karl Wettin
> Priority: Minor
> Attachments: spellchecker.diff
>
>
> Some minor changes to how the single token ngram spell checker in
> contrib/spellcheck, but nothing that breaks any old implementation I think.
> Also fixed the broken test.
> NgramPhraseSuggestier tokenizes a query and suggests combinations of the
> single token suggestions matrix.
> They must match as a query against an apriori index. By using a span near
> query (default) you get features like this:
> assertEquals("lost in translation", ngramSuggester.didYouMean("lost on
> translation"));
> If term position vectors are available it is possible to make it context
> sensitive (or what one may call it) to suggest a new term order.
> assertEquals("heroes might magic", ngramSuggester.didYouMean("magic light
> heros"));
> assertEquals("heroes of might and magic",
> ngramSuggester.didYouMean("heros on light and magik"));
> assertEquals("best game made", ngramSuggester.didYouMean("game best
> made"));
> assertEquals("game made", ngramSuggester.didYouMean("made game"));
> assertEquals("game made", ngramSuggester.didYouMean("made lame"));
> assertEquals("the game", ngramSuggester.didYouMean("the game"));
> assertEquals("in the fame", ngramSuggester.didYouMean("in the game"));
> assertEquals("game", ngramSuggester.didYouMean("same"));
> assertEquals(0, ngramSuggester.suggest("may game").size());
> SessionAnalyzedDictionary is the adaptive layer, that learns from how users
> changed their queries, what data they inspected, et c. It will automagically
> find and suggest synonyms, decomposed words, and probably a lot of other neat
> features I still have not detected.
> A bit depending on the situation, ignored suggestions get suppresed and
> followed suggestions get suggeted even more.
> assertEquals("the da vinci code",
> dictionary.didYouMean("thedavincicode"));
> assertEquals("the da vinci code", dictionary.didYouMean("the davinci
> code"));
> assertEquals("homm", dictionary.didYouMean("heroes of might and magic"));
> assertEquals("heroes of might and magic", dictionary.didYouMean("homm"));
> assertEquals("heroes of might and magic 2", dictionary.didYouMean("heroes
> of might and magic ii"));
> assertEquals("heroes of might and magic ii",
> dictionary.didYouMean("heroes of might and magic 2"));
> The adaptive layer is not yet(tm) persistent, but soft referenced so that the
> dictionary don't go eat up all your RAM.
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