23 jul 2010 kl. 08.30 skrev sk...@sloan.mit.edu:
Hi all, I have an interesting problem...instead of going from a query
to a document collection, is it possible to come up with the best fit
query for a given document collection (results)? "Best fit" being a
query which maximizes the hit scores of the resulting document
collection.
It would probably be helpful if you explained what it is you attempt
to achieve by doing this. Are you looking for MoreLikeThis?
How should I approach this? All suggestions appreciated.
How exepensive of an operation is this allowed to be? Can you waste
seconds, minutes, hours or days?
Are there any requirements on the precision and recall?
I would no matter what start with looking at the output from a feature
selection algorithm fed with the complete corpus divided in the two
classes "query factory set" and "all other documents".
The output will not tell you why the terms are important, just that
they probably are used when deciding when to classify documents as
part of query factory set or all other documents.
It's hard to say where to go from there.
Create a set of selected terms available in the query factory set.
Create a set of selected terms available in all other documents.
Create a set of selected terms only available in the query factory set.
Create a set of selected terms only available in all other documents.
See if there is a simple strategy based on above that produce a good
result.
If not you might want to look in to some evolving algorithm that
execute queries with permutations of selected features in order to
find the best query. Or if you have the resources, simply create all
permutation of queries.
If it works then I think all of the steps above could be optimized,
cached or simplified in several ways to make it speedy.
See Mahout, Weka (has a good experimenter/explorer GUI), Rapidminer,
etc for machine learning APIs.
It should not have to be too complicated to implement a gain ratio
feature selector using IndexReader if the term vector space is
available.
karl
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