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ECIR
2007
Springer

Query Hardness Estimation Using Jensen-Shannon Divergence Among Multiple Scoring Functions

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Query Hardness Estimation Using Jensen-Shannon Divergence Among Multiple Scoring Functions
We consider the issue of query performance, and we propose a novel method for automatically predicting the difficulty of a query. Unlike a number of existing techniques which are based on examining the ranked lists returned in response to perturbed versions of the query with respect to the given collection or perturbed versions of the collection with respect to the given query, our technique is based on examining the ranked lists returned by multiple scoring functions (retrieval engines) with respect to the given query and collection. In essence, we propose that the results returned by multiple retrieval engines will be relatively similar for “easy” queries but more diverse for “difficult” queries. By appropriately employing Jensen-Shannon divergence to measure the “diversity” of the returned results, we demonstrate a methodology for predicting query difficulty whose performance exceeds existing state-ofthe-art techniques on TREC collections, often remarkably so.
Javed A. Aslam, Virgiliu Pavlu
Added 29 Oct 2010
Updated 29 Oct 2010
Type Conference
Year 2007
Where ECIR
Authors Javed A. Aslam, Virgiliu Pavlu
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