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Towards robust query expansion: model selection in the language modeling framework

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SIGIR '07: Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval

Pages 729 - 730

https://doi.org/10.1145/1277741.1277880

Published: 23 July 2007 Publication History

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Abstract

We propose a language-model-based approach for addressing the performance robustness problem -- with respect to free-parameters' values -- of pseudo-feedback-based query-expansion methods. Given a query, we create a set of language models representing different forms of its expansion by varying the parameters' values of some expansion method; then, we select a single model using criteria originally proposed for evaluating the performance of using the original query, or for deciding whether to employ expansion at all. Experimental results show that these criteria are highly effective in selecting relevance language models that are not only significantly more effective than poor performing ones, but that also yield performance that is almost indistinguishable from that of manually optimized relevance models.

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Cited By

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Déjean SIonescu RMothe JUllah MHung CCerny TShin DBechini A(2020)Forward and backward feature selection for query performance predictionProceedings of the 35th Annual ACM Symposium on Applied Computing10.1145/3341105.3373904(690-697)Online publication date: 30-Mar-2020
https://dl.acm.org/doi/10.1145/3341105.3373904
Lu XKurland OCulpepper JCraswell NRom OFang YZhang YAllan JBalog KCarterette BGuo J(2019)Relevance Modeling with Multiple Query VariationsProceedings of the 2019 ACM SIGIR International Conference on Theory of Information Retrieval10.1145/3341981.3344224(27-34)Online publication date: 26-Sep-2019
https://dl.acm.org/doi/10.1145/3341981.3344224
Zendel OShtok ARaiber FKurland OCulpepper JPiwowarski BChevalier MGaussier EMaarek YNie JScholer F(2019)Information Needs, Queries, and Query Performance PredictionProceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3331184.3331253(395-404)Online publication date: 18-Jul-2019
https://dl.acm.org/doi/10.1145/3331184.3331253
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Index Terms

Towards robust query expansion: model selection in the language modeling framework
1. Information systems
  1. Information retrieval
    1. Retrieval models and ranking

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Information & Contributors

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Published In

SIGIR '07: Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval

July 2007

946 pages

ISBN:9781595935977

DOI:10.1145/1277741

General Chairs:
Wessel Kraaij
TNO, The Netherlands
,
Arjen P. de Vries
CWI, The Netherlands
,
Program Chairs:
Charles L. A. Clarke
University of Waterloo, Canada
,
Norbert Fuhr
University of Duisburg-Essen, Germany
,
Noriko Kando
National Institute of Informatics, Japan

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 23 July 2007

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SIGIR07

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SIGIR07: The 30th Annual International SIGIR Conference

July 23 - 27, 2007

Amsterdam, The Netherlands

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

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Cited By

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Déjean SIonescu RMothe JUllah MHung CCerny TShin DBechini A(2020)Forward and backward feature selection for query performance predictionProceedings of the 35th Annual ACM Symposium on Applied Computing10.1145/3341105.3373904(690-697)Online publication date: 30-Mar-2020
https://dl.acm.org/doi/10.1145/3341105.3373904
Lu XKurland OCulpepper JCraswell NRom OFang YZhang YAllan JBalog KCarterette BGuo J(2019)Relevance Modeling with Multiple Query VariationsProceedings of the 2019 ACM SIGIR International Conference on Theory of Information Retrieval10.1145/3341981.3344224(27-34)Online publication date: 26-Sep-2019
https://dl.acm.org/doi/10.1145/3341981.3344224
Zendel OShtok ARaiber FKurland OCulpepper JPiwowarski BChevalier MGaussier EMaarek YNie JScholer F(2019)Information Needs, Queries, and Query Performance PredictionProceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3331184.3331253(395-404)Online publication date: 18-Jul-2019
https://dl.acm.org/doi/10.1145/3331184.3331253
Roitman HCohen DHummel SCarterette BFang HLalmas MNie J(2016)From "More Like This" to "Better Than This"Proceedings of the 2016 ACM International Conference on the Theory of Information Retrieval10.1145/2970398.2970421(195-198)Online publication date: 12-Sep-2016
https://dl.acm.org/doi/10.1145/2970398.2970421
Thesprasith OJaruskulchai C(2016)Simple-phrase score for selective query expansion in health Information Retrieval2016 International Computer Science and Engineering Conference (ICSEC)10.1109/ICSEC.2016.7859913(1-6)Online publication date: Dec-2016
https://doi.org/10.1109/ICSEC.2016.7859913
Ionescu RChifu AMothe J(2015)DeShaToProceedings of the 22nd International Symposium on String Processing and Information Retrieval - Volume 930910.1007/978-3-319-23826-5_8(75-82)Online publication date: 1-Sep-2015
https://dl.acm.org/doi/10.1007/978-3-319-23826-5_8
Carmel DKurland OHersh WCallan JMaarek YSanderson M(2012)Query performance prediction for IRProceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval10.1145/2348283.2348540(1196-1197)Online publication date: 12-Aug-2012
https://dl.acm.org/doi/10.1145/2348283.2348540
Mahdabi PAndersson LKeikha MCrestani FHersh WCallan JMaarek YSanderson M(2012)Automatic refinement of patent queries using concept importance predictorsProceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval10.1145/2348283.2348353(505-514)Online publication date: 12-Aug-2012
https://dl.acm.org/doi/10.1145/2348283.2348353
Carpineto CRomano G(2012)A Survey of Automatic Query Expansion in Information RetrievalACM Computing Surveys10.1145/2071389.207139044:1(1-50)Online publication date: 1-Jan-2012
https://dl.acm.org/doi/10.1145/2071389.2071390
Carmel DYom-Tov E(2010)Estimating the Query Difficulty for Information RetrievalSynthesis Lectures on Information Concepts, Retrieval, and Services10.2200/S00235ED1V01Y201004ICR0152:1(1-89)Online publication date: Jan-2010
https://doi.org/10.2200/S00235ED1V01Y201004ICR015
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