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Mining dependency relations for query expansion in passage retrieval

Published: 06 August 2006 Publication History

Abstract

Classical query expansion techniques such as the local context analysis (LCA) make use of term co-occurrence statistics to incorporate additional contextual terms for enhancing passage retrieval. However, relevant contextual terms do not always co-occur frequently with the query terms and vice versa. Hence the use of such methods often brings in noise, which leads to reduced precision. Previous studies have demonstrated the importance of relationship analysis for natural language queries in passage retrieval. However, they found that without query expansion, the performance is not satisfactory for short queries. In this paper, we present two novel query expansion techniques that make use of dependency relation analysis to extract contextual terms and relations from external corpuses. The techniques are used to enhance the performance of density based and relation based passage retrieval frameworks respectively. We compare the performance of the resulting systems with LCA in a density based passage retrieval system (DBS) and a relation based system without any query expansion (RBS) using the factoid questions from the TREC-12 QA task. The results show that in terms of MRR scores, our relation based term expansion method with DBS outperforms the LCA by 9.81%, while our relation expansion method outperforms RBS by 17.49%.

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cover image ACM Conferences
SIGIR '06: Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
August 2006
768 pages
ISBN:1595933697
DOI:10.1145/1148170
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 06 August 2006

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Author Tags

  1. dependency parsing
  2. passage retrieval
  3. query expansion

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SIGIR06
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SIGIR06: The 29th Annual International SIGIR Conference
August 6 - 11, 2006
Washington, Seattle, USA

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Overall Acceptance Rate 792 of 3,983 submissions, 20%

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  • (2019)Efficient question classification and retrieval using category information and word embedding on cQA servicesJournal of Intelligent Information Systems10.1007/s10844-019-00556-x53:1(27-49)Online publication date: 1-Aug-2019
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