Article

A framework for selective query expansion

Authors:

Steve Cronen-Townsend,

Yun Zhou,

W. Bruce CroftAuthors Info & Claims

CIKM '04: Proceedings of the thirteenth ACM international conference on Information and knowledge management

Pages 236 - 237

https://doi.org/10.1145/1031171.1031220

Published: 13 November 2004 Publication History

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Abstract

Query expansion is a well-known technique that has been shown to improve <i>average</i> retrieval performance. This technique has not been used in many operational systems because of the fact that it can greatly degrade the performance of some individual queries. We show how comparison between language models of the unexpanded and expanded retrieval results can be used to predict when the expanded retrieval has strayed from the original sense of the query. In these cases, the unexpanded results are used while the expanded results are used in the remaining cases (where such straying is not detected). We evaluate this method on a wide variety of TREC collections.

References

[1]

W. B. Croft and J. Lafferty, editors. Language Modeling for Information Retrieval, pages 11--56. Kluwer, 2003.

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[2]

S. Cronen-Townsend, Y. Zhou, and W. B. Croft. Predicting query performance. In Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval, pages 299--306, August 2002.

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[3]

S. Cronen-Townsend, Y. Zhou, and W. B. Croft. A language modeling framework for selective query expansion. Technical Report IR-338, Center for Intelligent Information Retrieval, University of Massachusetts, 2004.

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[4]

F. Song and W. B. Croft. A general language model for informatioon retrieval. In Proceedings of the 22nd ACM SIGIR conference, pages 279--280, 1999.

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H. C. Taneja and R. K. Tuteja. Characterization of a quantitative-qualitative measure of relative information. Information Sciences, 33:217--222, 1984.

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Anand AV VSetty VAnand AHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)The Surprising Effectiveness of Rankers trained on Expanded QueriesProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657938(2652-2656)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657938
Göksel GArslan ADinçer B(2023)A selective approach to stemming for minimizing the risk of failure in information retrieval systemsPeerJ Computer Science10.7717/peerj-cs.11759(e1175)Online publication date: 10-Jan-2023
https://doi.org/10.7717/peerj-cs.1175
Mothe JUllah M(2023)Selective Query Processing: A Risk-Sensitive Selection of Search ConfigurationsACM Transactions on Information Systems10.1145/360847442:1(1-35)Online publication date: 21-Aug-2023
https://dl.acm.org/doi/10.1145/3608474
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Index Terms

A framework for selective query expansion
1. Information systems
  1. Information retrieval
    1. Information retrieval query processing

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Information & Contributors

Information

Published In

CIKM '04: Proceedings of the thirteenth ACM international conference on Information and knowledge management

November 2004

678 pages

ISBN:1581138741

DOI:10.1145/1031171

General Chair:
David Grossman
Illinois Institute of Technology
,
Program Chairs:
Luis Gravano
Columbia University
,
ChengXiang Zhai
University of Illinois at Urbana-Champaign
,
Otthein Herzog
University of Bremen, Germany
,
David A. Evans
Clairvoyance Corporation

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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Association for Computing Machinery

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Publication History

Published: 13 November 2004

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November 8 - 13, 2004

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Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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Anand AV VSetty VAnand AHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)The Surprising Effectiveness of Rankers trained on Expanded QueriesProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657938(2652-2656)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657938
Göksel GArslan ADinçer B(2023)A selective approach to stemming for minimizing the risk of failure in information retrieval systemsPeerJ Computer Science10.7717/peerj-cs.11759(e1175)Online publication date: 10-Jan-2023
https://doi.org/10.7717/peerj-cs.1175
Mothe JUllah M(2023)Selective Query Processing: A Risk-Sensitive Selection of Search ConfigurationsACM Transactions on Information Systems10.1145/360847442:1(1-35)Online publication date: 21-Aug-2023
https://dl.acm.org/doi/10.1145/3608474
Faggioli GFormal TLupart SMarchesin SClinchant SFerro NPiwowarski BYoshioka MKiseleva JAliannejadi M(2023)Towards Query Performance Prediction for Neural Information Retrieval: Challenges and OpportunitiesProceedings of the 2023 ACM SIGIR International Conference on Theory of Information Retrieval10.1145/3578337.3605142(51-63)Online publication date: 9-Aug-2023
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Mothe J(2022)Analytics Methods to Understand Information Retrieval Effectiveness—A SurveyMathematics10.3390/math1012213510:12(2135)Online publication date: 19-Jun-2022
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Zhang LJoho HFujita SYu HAl Hasan MXiong L(2022)Selectively Expanding Queries and Documents for News Background LinkingProceedings of the 31st ACM International Conference on Information & Knowledge Management10.1145/3511808.3557695(4687-4691)Online publication date: 17-Oct-2022
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Mazari ADjeffal A(2021)Hybrid Query Expansion Model Based on Pseudo Relevance Feedback and Semantic Tree for Arabic IRInternational Journal of Information Retrieval Research10.4018/IJIRR.28994912:1(1-16)Online publication date: 20-Oct-2021
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Mothe JUllah MDemartini GZuccon GCulpepper JHuang ZTong H(2021)Defining an Optimal Configuration Set for Selective Search Strategy - A Risk-Sensitive ApproachProceedings of the 30th ACM International Conference on Information & Knowledge Management10.1145/3459637.3482422(1335-1345)Online publication date: 26-Oct-2021
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Rexha ADragoni MKern RHuang RWu DMarchionini GHe DCunningham SHansen P(2020)A Neural-based Architecture For Small Datasets ClassificationProceedings of the ACM/IEEE Joint Conference on Digital Libraries in 202010.1145/3383583.3398535(319-327)Online publication date: 1-Aug-2020
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Elvina Mandala R(2020)Improving Effectiveness Information Retrieval System Using Pseudo Irrelevance Feedback2020 IEEE International Conference on Sustainable Engineering and Creative Computing (ICSECC)10.1109/ICSECC51444.2020.9557550(463-468)Online publication date: 16-Dec-2020
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Query expansion based on term distribution and DBpedia features

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Query expansion based on term distribution and DBpedia features

Improving query precision using semantic expansion

A new fuzzy logic-based query expansion model for efficient information retrieval using relevance feedback approach

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