Eks Dev commented on LUCENE-2089:
What about this,
it seems logically more appropriate to (human-entered) text objects than
Levenshtein distance, and it is (in theory) extremely fast; is DFA-distance
Is that only me who sees plain, vanilla bigram distance here? What is new or
better in StrikeAMatch compared to the first phase of the current SpellCehcker
(feeding PriorityQueue with candidates)?
If you need too use this, nothing simpler, you do not even need pair comparison
(aka traversal), just Index terms split into bigrams and search with standard
Autmaton trick is a neat one. Imo, the only thing that would work better is to
make term dictionary real trie (ternary, n-ary, dfa, makes no big diff). Making
TerrmDict some sort of trie/dfa would permit smart beam-search, even without
compiling query DFA. Beam search also makes implementation of better distances
possible (Weighted Edit distance without "metric constraint" ). I guess this is
going to be possible with Flex, Mike was allready talking about DFA Dictionary
It took a while to figure out the trick Robert pooled here, treating term
dictionary as another DFA due to the sortedness, nice.
> explore using automaton for fuzzyquery
> Key: LUCENE-2089
> URL: https://issues.apache.org/jira/browse/LUCENE-2089
> Project: Lucene - Java
> Issue Type: Wish
> Components: Search
> Reporter: Robert Muir
> Assignee: Mark Miller
> Priority: Minor
> Attachments: LUCENE-2089.patch, Moman-0.2.1.tar.gz, TestFuzzy.java
> Mark brought this up on LUCENE-1606 (i will assign this to him, I know he is
> itching to write that nasty algorithm)
> we can optimize fuzzyquery by using AutomatonTermsEnum, here is my idea
> * up front, calculate the maximum required K edits needed to match the users
> supplied float threshold.
> * for at least small common E up to some max K (1,2,3, etc) we should create
> a DFA for each E.
> if the required E is above our supported max, we use "dumb mode" at first (no
> seeking, no DFA, just brute force like now).
> As the pq fills, we swap progressively lower DFAs into the enum, based upon
> the lowest score in the pq.
> This should work well on avg, at high E, you will typically fill the pq very
> quickly since you will match many terms.
> This not only provides a mechanism to switch to more efficient DFAs during
> enumeration, but also to switch from "dumb mode" to "smart mode".
> i modified my wildcard benchmark to generate random fuzzy queries.
> * Pattern: 7N stands for NNNNNNN, etc.
> * AvgMS_DFA: this is the time spent creating the automaton (constructor)
> ||Pattern||Iter||AvgHits||AvgMS(old)||AvgMS (new,total)||AvgMS_DFA||
> as you can see, this prototype is no good yet, because it creates the DFA in
> a slow way. right now it creates an NFA, and all this wasted time is in
> NFA->DFA conversion.
> So, for a very long string, it just gets worse and worse. This has nothing to
> do with lucene, and here you can see, the TermEnum is fast (AvgMS -
> AvgMS_DFA), there is no problem there.
> instead we should just build a DFA to begin with, maybe with this paper:
> we can precompute the tables with that algorithm up to some reasonable K, and
> then I think we are ok.
> the paper references using http://portal.acm.org/citation.cfm?id=135907 for
> linear minimization, if someone wants to implement this they should not worry
> about minimization.
> in fact, we need to at some point determine if AutomatonQuery should even
> minimize FSM's at all, or if it is simply enough for them to be deterministic
> with no transitions to dead states. (The only code that actually assumes
> minimal DFA is the "Dumb" vs "Smart" heuristic and this can be rewritten as a
> summation easily). we need to benchmark really complex DFAs (i.e. write a
> regex benchmark) to figure out if minimization is even helping right now.
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