Hello,
I am trying to create a suggest search (search results are displayed while the
user is entering the query) for names, but the search should also give results
if the given name just sounds like an indexed name. However a perfect match
should be ranked higher than a similar sounding match.
I looked at the SpellChecker contrib, but this AFAIK cannot handle incomplete
names (edge n-grams).
So I came up with this idea and it would be great if anyone could tell me if
that is sensible or if there is a better way:
I create an analyzer to be run on the full names, which does the following
- lowercase
- build edge n-grams
put these terms in the field (this would handle correctly spelled input)
- run soundex on the n-grams
put there soundexed n-grams in the field as well
The incoming query will then also run through this analyzer with an or-default.
So a correct spelling will match the normal n-grams plus the soundexed n-grams
leading to a good score. A missspelled name would still match the soundexed
n-grams, leading to a somewhat lower score.
My current problem is that I don't know how to duplicate the tokens in the
analyzer so I can add them as normal n-grams and soundexed n-grams. I suppose
the TeeSinkTokenFilter will get me there, but I could not figure out how to add
all tokens back in one stream.
To recap, my questions are: Could this approach work to create a "fuzzy
suggest"? How do I use the TeeSinkTokenFilter to separate and recombine the
tokenstream.
I hope that was clear, thanks for your help!
Kai
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