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From "David Smiley (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (LUCENE-3842) Analyzing Suggester
Date Sat, 10 Nov 2012 23:13:12 GMT

    [ https://issues.apache.org/jira/browse/LUCENE-3842?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13494793#comment-13494793
] 

David Smiley commented on LUCENE-3842:
--------------------------------------

That TokenStreamToAutomaton is cool Mike!  I can put that to use in my FST text tagger work.
                
> Analyzing Suggester
> -------------------
>
>                 Key: LUCENE-3842
>                 URL: https://issues.apache.org/jira/browse/LUCENE-3842
>             Project: Lucene - Core
>          Issue Type: New Feature
>          Components: modules/spellchecker
>    Affects Versions: 3.6, 4.0-ALPHA
>            Reporter: Robert Muir
>            Assignee: Michael McCandless
>             Fix For: 4.1, 5.0
>
>         Attachments: LUCENE-3842.patch, LUCENE-3842.patch, LUCENE-3842.patch, LUCENE-3842.patch,
LUCENE-3842.patch, LUCENE-3842.patch, LUCENE-3842.patch, LUCENE-3842.patch, LUCENE-3842.patch,
LUCENE-3842.patch, LUCENE-3842.patch, LUCENE-3842.patch, LUCENE-3842.patch, LUCENE-3842.patch,
LUCENE-3842.patch, LUCENE-3842.patch, LUCENE-3842.patch, LUCENE-3842.patch, LUCENE-3842-TokenStream_to_Automaton.patch
>
>
> Since we added shortest-path wFSA search in LUCENE-3714, and generified the comparator
in LUCENE-3801,
> I think we should look at implementing suggesters that have more capabilities than just
basic prefix matching.
> In particular I think the most flexible approach is to integrate with Analyzer at both
build and query time,
> such that we build a wFST with:
> input: analyzed text such as ghost0christmas0past <-- byte 0 here is an optional token
separator
> output: surface form such as "the ghost of christmas past"
> weight: the weight of the suggestion
> we make an FST with PairOutputs<weight,output>, but only do the shortest path operation
on the weight side (like
> the test in LUCENE-3801), at the same time accumulating the output (surface form), which
will be the actual suggestion.
> This allows a lot of flexibility:
> * Using even standardanalyzer means you can offer suggestions that ignore stopwords,
e.g. if you type in "ghost of chr...",
>   it will suggest "the ghost of christmas past"
> * we can add support for synonyms/wdf/etc at both index and query time (there are tradeoffs
here, and this is not implemented!)
> * this is a basis for more complicated suggesters such as Japanese suggesters, where
the analyzed form is in fact the reading,
>   so we would add a TokenFilter that copies ReadingAttribute into term text to support
that...
> * other general things like offering suggestions that are more "fuzzy" like using a plural
stemmer or ignoring accents or whatever.
> According to my benchmarks, suggestions are still very fast with the prototype (e.g.
~ 100,000 QPS), and the FST size does not
> explode (its short of twice that of a regular wFST, but this is still far smaller than
TST or JaSpell, etc).

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