On the other hand, if n is not fixed, you still have a problem. As far as I read this list it seems, that Lucene reads a dictionary (of terms) into memory, and it also allocates one file handle for each of the acting terms. It implies you would not break the terms up into n-grams and, as a result, you would use a slow look-up over the dictionary. I do not know if I express it correctly, but my personal feeling is, that you would rather write your application from scratch.
BTW: If you have "nice terms", you could find all their n-grams occurencies in the dictionary, and compute a boost factor for each of the inverted lists. I.e., "bbc" is a term in a query, and for i-list of "abba", the factor is 1 (bigram "bb" is there), for i-list of "bbb", the factor is 2 ("bb" 2x). Then you use the Similarity class, and it is solved. Nevertheless, if the n-grams are not nice and the query is long, you will lost a lot of time in the dictionary look-up phase.
-g-
PS: I'm sorry for my English, just learning...
Jim Hargrave wrote:
Probably shouldn't have added that last bit. Our app isn't a DNA searcher. But DASG+Lev does look interesting.
Our app is a linguistic application. We want to search for sentences which have many ngrams in common and rank them based on the score below. Similar to the TELLTALE system (do a google search TELLTALE + ngrams) - but we are not interested in IR per se - we want to compute a score based on pure string similarity. Sentences are docs, ngrams are terms.
Jim
AFAIK Lucene is not able to look DNA strings up effectively. You would use DASG+Lev (see my previous post - 05/30/2003 1916CEST).[EMAIL PROTECTED] 06/05/03 03:55PM >>>
-g-
Jim Hargrave wrote:
Our application is a string similarity searcher where the query is an input string and we want to find all "fuzzy" variants of the input string in the DB. The Score is basically dice's coefficient: 2C/Q+D, where C is the number of terms (n-grams) in common, Q is the number of unique query terms and D is the number of unique document terms. Our documents will be sentences.
I know Lucene has a fuzzy search capability - but I assume this would be very slow since it must search through the entire term list to find candidates.
In order to do the calculation I will need to have 'C' - the number of terms in common between query and document. Is there an API that I can call to get this info? Any hints on what it will take to modify Lucene to handle these kinds of queries?
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