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Learning to rank query reformulations

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SIGIR '10: Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval

Pages 807 - 808

https://doi.org/10.1145/1835449.1835626

Published: 19 July 2010 Publication History

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Abstract

Query reformulation techniques based on query logs have recently proven to be effective for web queries. However, when initial queries have reasonably good quality, these techniques are often not reliable enough to identify the helpful reformulations among the suggested queries. In this paper, we show that we can use as few as two features to rerank a list of reformulated queries, or expanded queries to be specific, generated by a log-based query reformulation technique. Our results across five TREC collections suggest that there are consistently more useful reformulations in the first two positions in the new ranked list than there were initially, which leads to statistically significant improvements in retrieval effectiveness.

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Cited By

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Di Nunzio GFaggioli G(2021)A Study of a Gain Based Approach for Query Aspects in Recall Oriented TasksApplied Sciences10.3390/app1119907511:19(9075)Online publication date: 29-Sep-2021
https://doi.org/10.3390/app11199075
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
Mills CBavota GHaiduc SOliveto RMarcus ALucia A(2017)Predicting Query Quality for Applications of Text Retrieval to Software Engineering TasksACM Transactions on Software Engineering and Methodology10.1145/307884126:1(1-45)Online publication date: 26-May-2017
https://dl.acm.org/doi/10.1145/3078841
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Index Terms

Learning to rank query reformulations
1. Information systems
  1. Information retrieval
    1. Information retrieval query processing

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Information

Published In

SIGIR '10: Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval

July 2010

944 pages

ISBN:9781450301534

DOI:10.1145/1835449

General Chairs:
Fabio Crestani
University of Lugano, CH
,
Stéphane Marchand-Maillet
University of Geneva, CH
,
Program Chairs:
Hsin-Hsi Chen
National Taiwan University, TW
,
Efthimis N. Efthimiadis
University of Washington, USA
,
Jacques Savoy
University of Neuchatel, CH

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Published: 19 July 2010

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SIGIR

SIGIR '10: The 33rd International ACM SIGIR conference on research and development in Information Retrieval

July 19 - 23, 2010

Geneva, Switzerland

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SIGIR '10 Paper Acceptance Rate 87 of 520 submissions, 17%;

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

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Di Nunzio GFaggioli G(2021)A Study of a Gain Based Approach for Query Aspects in Recall Oriented TasksApplied Sciences10.3390/app1119907511:19(9075)Online publication date: 29-Sep-2021
https://doi.org/10.3390/app11199075
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
Mills CBavota GHaiduc SOliveto RMarcus ALucia A(2017)Predicting Query Quality for Applications of Text Retrieval to Software Engineering TasksACM Transactions on Software Engineering and Methodology10.1145/307884126:1(1-45)Online publication date: 26-May-2017
https://dl.acm.org/doi/10.1145/3078841
Shtok AKurland OCarmel D(2016)Query Performance Prediction Using Reference ListsACM Transactions on Information Systems10.1145/292679034:4(1-34)Online publication date: 9-Jun-2016
https://dl.acm.org/doi/10.1145/2926790
Cummins RBourdeau JHendler JNkambou RHorrocks IZhao B(2016)A Study of Retrieval Models for Long Documents and Queries in Information RetrievalProceedings of the 25th International Conference on World Wide Web10.1145/2872427.2883009(795-805)Online publication date: 11-Apr-2016
https://dl.acm.org/doi/10.1145/2872427.2883009
Arguello JAvula SDiaz F(2016)Using Query Performance Predictors to Improve Spoken QueriesAdvances in Information Retrieval10.1007/978-3-319-30671-1_23(309-321)Online publication date: 2016
https://doi.org/10.1007/978-3-319-30671-1_23
Gupta MBendersky MBaeza-Yates RLalmas MMoffat ARibeiro-Neto B(2015)Information Retrieval with Verbose QueriesProceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/2766462.2767877(1121-1124)Online publication date: 9-Aug-2015
https://dl.acm.org/doi/10.1145/2766462.2767877
Raman KBennett PCollins-Thompson K(2014)Understanding Intrinsic Diversity in Web SearchACM Transactions on Information Systems10.1145/262955332:4(1-45)Online publication date: 28-Oct-2014
https://dl.acm.org/doi/10.1145/2629553
Cummins R(2014)Document Score Distribution Models for Query Performance Inference and PredictionACM Transactions on Information Systems10.1145/255917032:1(1-28)Online publication date: 1-Jan-2014
https://dl.acm.org/doi/10.1145/2559170
Raman KBennett PCollins-Thompson KJones GSheridan PKelly Dde Rijke MSakai T(2013)Toward whole-session relevanceProceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval10.1145/2484028.2484089(463-472)Online publication date: 28-Jul-2013
https://dl.acm.org/doi/10.1145/2484028.2484089
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Recommendations

Query reformulation using anchor text

Query reformulation, search performance, and term suggestion devices in question-answering tasks

Exploiting query logs for cross-lingual query suggestions

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