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Posted to dev@lucene.apache.org by "Robert Muir (JIRA)" <ji...@apache.org> on 2012/06/28 14:03:44 UTC
[jira] [Updated] (LUCENE-3842) Analyzing Suggester
[ https://issues.apache.org/jira/browse/LUCENE-3842?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Robert Muir updated LUCENE-3842:
--------------------------------
Attachment: LUCENE-3842.patch
no changes: just syncing up with trunk
> 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, 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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