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2011
ACM

Parameterized concept weighting in verbose queries

7 years 9 months ago
Parameterized concept weighting in verbose queries
The majority of the current information retrieval models weight the query concepts (e.g., terms or phrases) in an unsupervised manner, based solely on the collection statistics. In this paper, we go beyond the unsupervised estimation of concept weights, and propose a parameterized concept weighting model. In our model, the weight of each query concept is determined using a parameterized combination of diverse importance features. Unlike the existing supervised ranking methods, our model learns importance weights not only for the explicit query concepts, but also for the latent concepts that are associated with the query through pseudo-relevance feedback. The experimental results on both newswire and web TREC corpora show that our model consistently and significantly outperforms a wide range of state-of-the-art retrieval models. In addition, our model significantly reduces the number of latent concepts used for query expansion compared to the nonparameterized pseudo-relevance feedbac...
Michael Bendersky, Donald Metzler, W. Bruce Croft
Added 17 Sep 2011
Updated 17 Sep 2011
Type Journal
Year 2011
Where SIGIR
Authors Michael Bendersky, Donald Metzler, W. Bruce Croft
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