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From "Robert Muir (Commented) (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (LUCENE-3842) Analyzing Suggester
Date Sun, 04 Mar 2012 16:49:59 GMT

    [ https://issues.apache.org/jira/browse/LUCENE-3842?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13221932#comment-13221932
] 

Robert Muir commented on LUCENE-3842:
-------------------------------------

{quote}
I looked at the patch but I don't fully get what it does. Looks like a combination of state
sequence unions, am I right?
{quote}

Well the conversion should ultimately be more useful for the suggester to intersect with the
FST than a tokenstream, because a tokenstream is like a word-level automaton, if dogs is a
synonym for dog, then we have:
smelly dog|dogs(positionIncrement=0). 

So for our intersection, we would prefer it to be a deterministic at 'character' (byte) level
instead. So the conversion should produce an automaton of: smelly dog(s?) in regex notation...
this is easier to work with.

at index time its useful too, because in the FST we only care about all the possible byte
strings, so this should be easier to enumerate than a tokenstream (especially if you consider
multiword synonyms, decompounded terms etc where some span across many).

                
> 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
>
>
> 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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