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https://issues.apache.org/jira/browse/LUCENE-3842?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13278267#comment-13278267
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Michael McCandless commented on LUCENE-3842:
--------------------------------------------
Hi Sudarshan, sorry that was my bad: I had svn mv'd RollingBuffer but when I
created the patch I failed to pass --show-copies-as-adds to svn ... so you have
to do that mv yourself before applying the patch...
> Analyzing Suggester
> -------------------
>
> Key: LUCENE-3842
> URL: https://issues.apache.org/jira/browse/LUCENE-3842
> Project: Lucene - Java
> Issue Type: New Feature
> Components: modules/spellchecker
> Affects Versions: 3.6, 4.0
> Reporter: Robert Muir
> Attachments: LUCENE-3842-TokenStream_to_Automaton.patch,
> LUCENE-3842.patch, LUCENE-3842.patch, LUCENE-3842.patch, LUCENE-3842.patch,
> LUCENE-3842.patch, LUCENE-3842.patch, LUCENE-3842.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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