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From "Robert Muir (Commented) (JIRA)" <>
Subject [jira] [Commented] (LUCENE-3842) Analyzing Suggester
Date Sun, 04 Mar 2012 15:02:00 GMT


Robert Muir commented on LUCENE-3842:

Wow, thats awesome Mike... its so small too!

I think we can integrate these two patches into one, with your patch and your addStartNode
the AnalyzingSuggester should be able to support both index-time and query-time synonyms/wdf/whatever
crazy stuff we throw at it :)

but we still need the "enumerate all paths from this automaton" method...

Brics has some code for this (puts all the accepted strings into a set). we should be able
to do
something similar, to create a set of bytesref? 

But really, i'm not sure we need a 'general' method for this. i think we should just have
an enumerator
for *finite* automata (e.g. tokenstream) as we can probably make this a 'real' enum rather
than creating
a massive list/set, we dont need the set deduplication at all either, because its finite.

> 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
> 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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