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From "Sudarshan Gaikaiwari (JIRA)" <>
Subject [jira] [Commented] (LUCENE-3842) Analyzing Suggester
Date Sat, 30 Jun 2012 16:19:44 GMT


Sudarshan Gaikaiwari commented on LUCENE-3842:

bq. maybe we can tie-break instead by the surface form?

The FST construction guarantees that the input paths leading to different nodes are unique,
while I don't think we have such a guarantee about the surface form.

I have attached a patch that modifies the intersectPrefixPaths method to keep track of the
input paths as the automaton and FST are intersected. Please let me know if that look ok to
> Analyzing Suggester
> -------------------
>                 Key: LUCENE-3842
>                 URL:
>             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, 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
> 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
> * 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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