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Modeling reformulation using query distributions

Published:17 May 2013Publication History
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Abstract

Query reformulation modifies the original query with the aim of better matching the vocabulary of the relevant documents, and consequently improving ranking effectiveness. Previous models typically generate words and phrases related to the original query, but do not consider how these words and phrases would fit together in actual queries. In this article, a novel framework is proposed that models reformulation as a distribution of actual queries, where each query is a variation of the original query. This approach considers an actual query as the basic unit and thus captures important query-level dependencies between words and phrases. An implementation of this framework that only uses publicly available resources is proposed, which makes fair comparisons with other methods using TREC collections possible. Specifically, this implementation consists of a query generation step that analyzes the passages containing query words to generate reformulated queries and a probability estimation step that learns a distribution for reformulated queries by optimizing the retrieval performance. Experiments on TREC collections show that the proposed model can significantly outperform previous reformulation models.

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    • Published in

      cover image ACM Transactions on Information Systems
      ACM Transactions on Information Systems  Volume 31, Issue 2
      May 2013
      180 pages
      ISSN:1046-8188
      EISSN:1558-2868
      DOI:10.1145/2457465
      Issue’s Table of Contents

      Copyright © 2013 ACM

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      Publication History

      • Published: 17 May 2013
      • Accepted: 1 November 2012
      • Revised: 1 June 2012
      • Received: 1 November 2011
      Published in tois Volume 31, Issue 2

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