Estimating topical context by diverging from external resources

Abstract : Improving query understanding is crucial for providing the user with information that suits her needs. To this end, the retrieval system must be able to deal with several sources of knowledge from which it could infer a topical context. The use of external sources of information for improving document retrieval has been extensively studied. Improvements with either structured or large sets of data have been reported. However, in these studies resources are often used separately and rarely combined together. We experiment in this paper a method that discounts documents based on their weighted divergence from a set of external resources. We present an evaluation of the combination of four resources on two standard TREC test collections. Our proposed method significantly outperforms a state-of-the-art Mixture of Relevance Models on one test collection, while no significant differences are detected on the other one.
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Conference papers
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https://hal.archives-ouvertes.fr/hal-01314962
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Submitted on : Thursday, May 12, 2016 - 2:25:06 PM
Last modification on : Tuesday, April 2, 2019 - 2:03:40 AM

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Romain Deveaud, Eric Sanjuan, Patrice Bellot. Estimating topical context by diverging from external resources. SIGIR'13, Jul 2013, Dublin, Ireland. ⟨10.1145/2484028.2484148⟩. ⟨hal-01314962⟩

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