When you do an explain on these results, what are all the factors that contribute to the score?

Could you increase the coord() factor in a custom Similarity implementation, to give a bigger boost to documents that have more matching terms? The point of coord is to give a little bump to those docs that have more terms from the query in a given document. Sounds like you want a bigger bump once you have multiple query terms in a document. Would this work for you?

Also, below...

On Jul 3, 2007, at 3:20 PM, Tim Sturge wrote:

That's true, but it's not clear that I want phrase matches. Consider for example:

"Lucene Download" as a query. I want something that strongly references "Lucene" (in the title) and strongly references "Download" but "Download Lucene" or "Lucene Project Download" are better than some page that happens to contain the exact phrase.

Not sure I follow you here. By strongly references, do you mean there are multiple occurrences of Download? Why would those alternatives be better than an exact phrase match?


Other examples are "camera review" or "Gonzales scandal"; there's a whole class of "subject <modifier>" queries that are not really phrase based, and my corpus isn't large enough to necessarily contain the phrase anyway.

I agree that many two or three word queries are really best matched by phrases, but not all. Is it common to use a phrase query with high slop to overcome the unequal weighting problem?

Also, my interface does support "\"John Bush\"" (ie the user can quote the phrase if they like) and I would prefer not to infer automatically that they meant to do so.

Tim

Jason Pump wrote:
You're not using any type of phrase search. Try ->

( (title:"John Bush"^4.0) OR (body:"John Bush") ) AND ( (title:John^4.0 body:John) AND (title:Bush^4.0 body:Bush) )

or maybe

( (title:"John Bush"~4^4.0) OR (body:"John Bush"~4) ) AND ( (title:John^4.0 body:John) AND (title:Bush^4.0 body:Bush) )



Tim Sturge wrote:
I'm following myself up here to ask if anyone has experience or code with a BooleanQuery that weights the terms it encounters on a product basis rather than a sum basis.

This would effectively compute the geometric mean of the term score (rather than the arithmetic mean) and would give me more "middle bias". It also has the great advantage that it automatically implements AND (as something without the term has a score of 0.0 which causes the query to go to 0.0 as well.)

I'm curious though why this doesn't already exist. Is it a bad idea in general (that I will discover once I implement it and look at the results?) or does it make searching a lot slower?

Thanks,

Tim

Tim Sturge wrote:
I have an index with two different sources of information, one small but of high quality (call it "title"), and one large, but of lower quality (call it "body"). I give boosts to certain documents related to their popularity (this is very similar to what one would do indexing the web).

The problem I have is a query like "John Bush". I translate that into " (title:John^4.0 body:John) AND (title:Bush^4.0 body:Bush) ". But the results I get are:

1. George Bush
...
4. John Kerry
...
10. John Bush

The reason is (looking at explain) that George Bush is scored:
169 = sum(
1 =  <match in body with tiny norm for "John">
)
168 = sum(
    160 = <title match for "Bush">
    8 = <body match for "Bush">
)
)

and John Kerry is similar but reversed. Poor old "John Bush" only scores:

72 = sum(
 40 = (<title match for "John">+<body match>)
 32 = (<title match for "Bush">+ <body match>)
)

because his initial boost was only 1/4 of George's.

The question I have is, how can tell the searcher to care about "balance"? I really want the score over 2 terms to be more like (sqrt(X)+sqrt(Y))^2 or maybe even exp(log(X)+log(Y)) rather than just X+Y. Is that supported in some obvious way, or is there some other way to phrase my query to say "I want both terms but they should both be important if possible?"

Thanks,

Tim







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Grant Ingersoll
http://www.grantingersoll.com/
http://lucene.grantingersoll.com
http://www.paperoftheweek.com/



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