hi, this could probably split into two threads but for context let's start
it in a single discussion;
Recently I was looking at the search quality of Lucene - Recall and
Precision, focused at [EMAIL PROTECTED],5,10,20 and, mainly, MAP.
-- Part 1 --
I found out that quality can be enhanced by modifying the doc length
normalization, and by changing the tf() computation to also consider the
average tf() in a single document.
For the first change, logic is that Lucene's default length normalization
punishes long documents too much. I found contrib's sweet-spot-similarity
helpful here, but not enough. I found that a better doc-length
normalization method is one that considers collection statistics - e.g.
average doc length. The nice problem with such an approach is that you
don't know the average length at indexing time, and it changes as the index
evolves. The static nature of norms computation (and API) in Lucene is,
while efficient, an obstacle for global computations. Another issue here is
that applications often split documents into fields from reasons that are
not "pure IR", for instance - content field and title field, just to be
able to boost the title by (say) 3, but in fact, there is no "IR'ish"
difference between finding the searched text in the title field or in the
body field - they really serve/answer the same information need. For that
matter, I believe that using a single document length when searching all
these fields is more "accurate".
For the second change logic, - assume two documents, doc1 containing 10
"A"'s, 10 "B"'s, and 10 "Z"'s, and doc2 containing "A" to "T" and 10 "Z"'s.
Both doc1 and doc2 are of length 30. Searching for "Z", in both doc1 and
doc2 tf("Z")=10. So, currently, doc1 and doc2 score the same for "Z", but
the "truth" is that "Z" is much more representing/important in doc2 than it
is in doc1, because its frequency in doc2 is 10 times more than all the
other words in that doc, while in doc1 it is the same as the other words in
that doc. If you agree about the potential improvement here, again, a nice
problem is that current Similarity API does not even allow to consider this
info (the average term frequency in the specific document) because
Similarity.tf(int/float freq) takes only the frequency param. One way to
open way for such computation is to add an "int docid" param to the
Similarity class, but then the implementation of that class becomes
IndexReader aware.
Both modifications above have, in addition to API implications also
performance implications, mainly search performance, and I would like to
get some feedback on what people think about going in this direction...
first the "if", only then the "how"...
-- Part 2 --
It is very important that we would be able to assess the search quality in
a repeatable manner - so that anyone can repeat the quality tests, and
maybe find ways to improve them. (This would also allow to verify the
"improvements claims" above...). This capability seems like a natural part
of the benchmark package. I started to look at extending the benchmark
package with search quality module, that would open an index (or first
create one), run a set of queries (similar to the performance benchmark),
and compute and report the set of known statistics mentioned above and
more. Such a module depends on input data - documents, queries, and
judgements. And that's my second question. We don't have to invent this
data - TREC has it already, and it is getting wider every year as there are
more judgements. So, theoretically we could use TREC data. One problem here
is that TREC data should be purchased. Not sure that this is a problem - it
is OK if we provide the mechanism to use this data for those who have it
(Universities, for one). The other problem is that it is not clear to me
what can one legally say on a certain system's results on TREC data. I
would like the Search Quality Web page of Lucene to say something like:
"MAP of XYZ for Track Z of TREC 2004", and then a certain submitted patch
to say "I improved to 1.09*XYZ". But would that be legal? I just re-read
their "Agreement Concerning Dissemination of TREC Results" -
http://trec.nist.gov/act_part/forms/noads.html - and I am not feeling
smarter about this.
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Thoughts?
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