research-article

Robust query rewriting using anchor data

Authors:

Bodo Billerbeck,

Dennis Fetterly,

Marc NajorkAuthors Info & Claims

WSDM '13: Proceedings of the sixth ACM international conference on Web search and data mining

Pages 335 - 344

https://doi.org/10.1145/2433396.2433440

Published: 04 February 2013 Publication History

Abstract

Query rewriting algorithms can be used as a form of query expansion, by combining the user's original query with automatically generated rewrites. Rewriting algorithms bring linguistic datasets to bear without the need for iterative relevance feedback, but most studies of rewriting have used proprietary datasets such as large-scale search logs. By contrast this paper uses readily available data, particularly ClueWeb09 link text with over 1.2 billion anchor phrases, to generate rewrites. To avoid overfitting, our initial analysis is performed using Million Query Track queries, leading us to identify three algorithms which perform well. We then test the algorithms on Web and newswire data. Results show good properties in terms of robustness and early precision.

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Cited By

Fröbe MGünther SProbst MPotthast MHagen M(2022)The Power of Anchor Text in the Neural Retrieval EraAdvances in Information Retrieval10.1007/978-3-030-99736-6_38(567-583)Online publication date: 5-Apr-2022
https://doi.org/10.1007/978-3-030-99736-6_38
Brusilovsky PSmyth BShapira B(2018)Social SearchSocial Information Access10.1007/978-3-319-90092-6_7(213-276)Online publication date: 3-May-2018
https://doi.org/10.1007/978-3-319-90092-6_7
Hwang SKim SHe YElnikety SChoi S(2016)Prediction and Predictability for Search Query AccelerationACM Transactions on the Web10.1145/294378410:3(1-28)Online publication date: 16-Aug-2016
https://dl.acm.org/doi/10.1145/2943784
Show More Cited By

Index Terms

Robust query rewriting using anchor data
1. Information systems
  1. Information retrieval

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cover image ACM Conferences

WSDM '13: Proceedings of the sixth ACM international conference on Web search and data mining

February 2013

816 pages

ISBN:9781450318693

DOI:10.1145/2433396

General Chairs:
Stefano Leonardi
Sapienza University of Rome, Italy
,
Alessandro Panconesi
Sapienza University of Rome, Italy
,
Program Chairs:
Paolo Ferragina
University of Pisa, Italy
,
Aristides Gionis
Yahoo! Research, Barcelona, Spain

Copyright © 2013 ACM.

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Published: 04 February 2013

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WSDM 2013

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WSDM 2013: Sixth ACM International Conference on Web Search and Data Mining

February 4 - 8, 2013

Rome, Italy

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Cited By

Fröbe MGünther SProbst MPotthast MHagen M(2022)The Power of Anchor Text in the Neural Retrieval EraAdvances in Information Retrieval10.1007/978-3-030-99736-6_38(567-583)Online publication date: 5-Apr-2022
https://doi.org/10.1007/978-3-030-99736-6_38
Brusilovsky PSmyth BShapira B(2018)Social SearchSocial Information Access10.1007/978-3-319-90092-6_7(213-276)Online publication date: 3-May-2018
https://doi.org/10.1007/978-3-319-90092-6_7
Hwang SKim SHe YElnikety SChoi S(2016)Prediction and Predictability for Search Query AccelerationACM Transactions on the Web10.1145/294378410:3(1-28)Online publication date: 16-Aug-2016
https://dl.acm.org/doi/10.1145/2943784
Ren XCheng TGangemi ALeonardi SPanconesi A(2015)Synonym Discovery for Structured Entities on Heterogeneous GraphsProceedings of the 24th International Conference on World Wide Web10.1145/2740908.2745396(443-453)Online publication date: 18-May-2015
https://dl.acm.org/doi/10.1145/2740908.2745396
Bing LLam WWong TJameel S(2015)Web Query Reformulation via Joint Modeling of Latent Topic Dependency and Term ContextACM Transactions on Information Systems10.1145/269966633:2(1-38)Online publication date: 17-Feb-2015
https://dl.acm.org/doi/10.1145/2699666
Jeon MKim SHwang SHe YElnikety SCox ARixner SGeva STrotman ABruza PClarke CJärvelin K(2014)Predictive parallelizationProceedings of the 37th international ACM SIGIR conference on Research & development in information retrieval10.1145/2600428.2609572(253-262)Online publication date: 3-Jul-2014
https://dl.acm.org/doi/10.1145/2600428.2609572
Bakhtin AUstinovskiy YSerdyukov PHe QIyengar ANejdl WPei JRastogi R(2013)Predicting the impact of expansion terms using semantic and user interaction featuresProceedings of the 22nd ACM international conference on Information & Knowledge Management10.1145/2505515.2507872(1825-1828)Online publication date: 27-Oct-2013
https://dl.acm.org/doi/10.1145/2505515.2507872

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