research-article

Modeling reformulation using query distributions

Authors:

W. Bruce CroftAuthors Info & Claims

ACM Transactions on Information Systems (TOIS), Volume 31, Issue 2

Article No.: 6, Pages 1 - 34

https://doi.org/10.1145/2457465.2457466

Published: 17 May 2013 Publication History

Abstract

Query reformulation modifies the original query with the aim of better matching the vocabulary of the relevant documents, and consequently improving ranking effectiveness. Previous models typically generate words and phrases related to the original query, but do not consider how these words and phrases would fit together in actual queries. In this article, a novel framework is proposed that models reformulation as a distribution of actual queries, where each query is a variation of the original query. This approach considers an actual query as the basic unit and thus captures important query-level dependencies between words and phrases. An implementation of this framework that only uses publicly available resources is proposed, which makes fair comparisons with other methods using TREC collections possible. Specifically, this implementation consists of a query generation step that analyzes the passages containing query words to generate reformulated queries and a probability estimation step that learns a distribution for reformulated queries by optimizing the retrieval performance. Experiments on TREC collections show that the proposed model can significantly outperform previous reformulation models.

References

[1]

Bendersky, M. and Croft, W. B. 2008. Discovering key concepts in verbose queries. In Proceedings of the Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR'08). 491--498.

Digital Library

[2]

Bendersky, M., Metzler, D., and Croft, W. B. 2010. Learning concept importance using a weighted dependence model. In Proceedings of the ACM International Conference on Web Search and Data Mining (WSDM'10). 31--40.

Digital Library

[3]

Bendersky, M., Smith, D. A., and Croft, W. B. 2009. Two-stage query segmentation for information retrieval. In Proceedings of the Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR'09). 810--811.

Digital Library

[4]

Bergsma, S. and Wang, Q. I. 2007. Learning noun phrase query segmentation. In Proceedings of the Conference on Empirical Methods on Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL'07). 819--826.

[5]

Burges, C., Shaked, T., Renshaw, E., Lazier, A., Deeds, M., Hamilton, N., and Hullender, G. 2005. Learning to rank using gradient descent. In Proceedings of the International Conference on Machine Learning (ICML'05). 89--96.

Digital Library

[6]

Byrd, R. H., Nocedal, J., and Schnabel, R. B. 1994. Rrepresentations of quasi-newton matrices and their use in limited memory methods. Math. Program. 63, 2, 129--156.

Digital Library

[7]

Cao, G., Nie, J. Y., Gao, J., and Robertson, S. 2008. Selecting good expansion terms for pseudorelevance feedback. In Proceedings of the Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR'08). 243--250.

Digital Library

[8]

Cao, Z., Qin, T., Liu, T.-Y., Tsai, M.-F., and Li, H. 2007. Learning to rank: from pairwise approach to listwise approach. In Proceedings of the International Conference on Machine Learning (ICML'07). ACM, 129--136.

Digital Library

[9]

Collins-Thompson, K. 2008. Robust model estimation methods for information retrieval. Ph.D. thesis, Carnegie Mellon University.

[10]

Collins-Thompson, K. and Callan, J. 2007. Estimation and use of uncertainty in pseudo-relevance feedback. In Proceedings of the Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR'07). 303--310.

Digital Library

[11]

Crouch, C. J. and Yang, B. 1992. Experiments in automatic statistical thesaurus construction. In Proceedings of the Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR'92). 77--88.

Digital Library

[12]

Cummins, R., Lalmas, M., Oriordan, C., and Jose, J. 2011. Navigating the user query space. In Proceedings of the 18th International Conference on String Processing and Information Retrieval. Springer, 380--385.

Digital Library

[13]

Dang, V. and Croft, W. B. 2010. Query reformulation using anchor text. In Proceedings of the ACM International Conference on Web Search and Data Mining (WSDM'10). 41--50.

Digital Library

[14]

Freund, Y., Iyer, R. D., Schapire, R. E., and Singer, Y. 2003. An efficient boosting algorithm for combining preferences. J. Mach. Learn. Res. 4, 933--969.

Digital Library

[15]

Guo, J., Xu, G., Li, H., and Cheng, X. 2008. A unified and discriminative model for query refinement. In Proceedings of the Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR'08). 379--386.

Digital Library

[16]

Herbrich, R., Graepel, T., and Obermayer, K. 2000. Large Margin Rank Boundaries for Ordinal Regression. MIT Press, Cambridge, MA.

[17]

Huang, J., Gao, J., Miao, J., Li, X., Wang, K., Behr, F., and Giles, C. L. 2010. Exploring web scale language models for search query processing. In Proceedings of the International Conference on World Wide Web (WWW'10). ACM, 451--460.

Digital Library

[18]

Ide, E. 1971. New experiments in relevance feedback. In The SMART Retrieval System: Experiments in Automatic Document Processing, Prentice Hall, Englewood Cliffs, NJ.

[19]

Jones, R. and Fain, D. C. 2003. Query word deletion prediction. In Proceedings of the Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR'03). 435--436.

Digital Library

[20]

Jones, R., Rey, B., Madani, O., and Greiner, W. 2006. Generating query substitutions. In Proceedings of the International Conference on World Wide Web (WWW'06). 387--396.

Digital Library

[21]

Lafferty, J. and Zhai, C. 2001. Document language models, query models, and risk minimization for information retrieval. In Proceedings of the International Conference on Machine Learning (ICML'01). 111--119.

Digital Library

[22]

Lang, H., Metzler, D., Wang, B., and Li, J.-T. 2010. Improved latent concept expansion using hierarchical Markov random fields. In Proceedings of the International Conference on Information and Knowledge Management (CIKM'10). 249--258.

Digital Library

[23]

Lavrenko, V. and Croft, W. B. 2001. Relevance based language models. In Proceedings of the Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR'01). 120--127.

Digital Library

[24]

Liu, X. and Croft, W. B. 2002. Passage retrieval based on language models. In Proceedings of the International Conference on Information and Knowledge Management (CIKM'02). 375--382.

Digital Library

[25]

Lv, Y. and Zhai, C. 2010. Positional relevance model for pseudo-relevance feedback. In Proceedings of the Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR'10). 579--586.

Digital Library

[26]

Lv, Y., Zhai, C., and Chen, W. 2011. A boosting approach to improving pseudo-relevance feedback. In Proceedings of the International Conference on Machine Learning (ICML'11). ACM, 165--174.

Digital Library

[27]

Metzler, D. and Croft, W. B. 2004. Combining the language model and inference network approaches to retrieval. Inf. Process. Manage. 40, 5, 735--750.

Digital Library

[28]

Metzler, D. and Croft, W. B. 2005. A Markov random field model for term dependencies. In Proceedings of the Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR'05). 472--479.

Digital Library

[29]

Metzler, D. and Croft, W. B. 2007. Latent concept expansion using Markov random fields. In Proceedings of the Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR'07). 311--318.

Digital Library

[30]

Peng, F., Ahmed, N., Li, X., and Lu, Y. 2007. Context sensitive stemming for web search. In Proceedings of the Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR'07). 639--646.

Digital Library

[31]

Ponte, J. M. and Croft, W. B. 1998. A language modeling approach to information retrieval. In Proceedings of the Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR'98). 275--281.

Digital Library

[32]

Porter, M. F. 1980. An algorithm for suffix stripping. Program 14, 3, 130--137.

[33]

Qin, T., Liu, T.-Y., Zhang, X.-D., Wang, D.-S., Xiong, W.-Y., and Li, H. 2008. Learning to rank relational objects and its application to web search. In Proceedings of the International Conference on World Wide Web (WWW'08). 407--416.

Digital Library

[34]

Qiu, Y. and Frei, H. P. 1993. Concept based query expansion. In Proceedings of the Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR'93). 160--169.

Digital Library

[35]

Rocchio, J. J. 1971. Relevance feedback in information retrieval. In The SMART Retrieval System: Experiments in Automatic Document Processing, Prentice Hall, Englewood Cliffs, NJ.

[36]

Sheldon, D., Shokouhi, M., Szummer, M., and Craswell, N. 2011. Lambdamerge: merging the results of query reformulations. In Proceedings of the ACM International Conference on Web Search and Data Mining (WSDM'11). 795--804.

Digital Library

[37]

Soskin, N., Kurland, O., and Domshlak, C. 2009. Navigating in the dark: modeling uncertainty in ad hoc retrieval using multiple relevance models. In Proceedings of the International Conference on the Theory of Information Retrieval (ICTIR'09). 79--91.

Digital Library

[38]

Svore, K. M., Kanani, P. H., and Khan, N. 2010. How good is a span of terms&quest;: Exploiting proximity to improve web retrieval. In Proceedings of the Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR'10). 155--161.

Digital Library

[39]

Tan, B. and Peng, F. 2008. Unsupervised query segmentation using generative language models and Wikipedia. In Proceedings of the International Conference on World Wide Web (WWW'08). 347--356.

Digital Library

[40]

Wang, L., Lin, J., and Metzler, D. 2010. Learning to efficiently rank. In Proceedings of the Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR'10). 138--145.

Digital Library

[41]

Wang, X. and Zhai, C. 2008. Mining term association patterns from search logs for effective query reformulation. In Proceedings of the International Conference on Information and Knowledge Management (CIKM'08). 479--488.

Digital Library

[42]

Xu, J. and Croft, W. B. 2000. Improving the effectiveness of information retrieval with local context analysis. ACM Trans. Inf. Syst. 18, 1, 79--112.

Digital Library

[43]

Xu, J. and Li, H. 2007. Adarank: a boosting algorithm for information retrieval. In Proceedings of the Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR'07). 391--398.

Digital Library

[44]

Xu, Y., Jones, G. J., and Wang, B. 2009. Query dependent pseudo-relevance feedback based on Wikipedia. In Proceedings of the Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR'09). 59--66.

Digital Library

[45]

Xue, X. and Croft, W. B. 2010. Representing queries as distributions. In Proceedings of the Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR'10) Workshop on Query Representation and Understanding. 9--12.

[46]

Xue, X. and Croft, W. B. 2011. Modeling subset distributions for verbose queries. In Proceedings of the Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR'11). 1133--1134.

Digital Library

[47]

Xue, X., Croft, W. B., and Smith, D. A. 2010. Modeling reformulation using passage analysis. In Proceedings of the International Conference on Information and Knowledge Management (CIKM'10). 1497--1500.

Digital Library

[48]

Zhai, C. and Lafferty, J. 2001a. Model-based feedback in the language modeling approach to information retrieval. In Proceedings of the International Conference on Information and Knowledge Management (CIKM'01). 403--410.

Digital Library

[49]

Zhai, C. and Lafferty, J. 2001b. A study of smoothing methods for language models applied to ad hoc information retrieval. In Proceedings of the Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR'01). 334--342.

Digital Library

Cited By

Zhao ZDou ZGuo YCao ZCheng XSingh ASun YAkoglu LGunopulos DYan XKumar ROzcan FYe J(2023)Improving Search Clarification with Structured Information Extracted from Search ResultsProceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3580305.3599389(3549-3558)Online publication date: 6-Aug-2023
https://dl.acm.org/doi/10.1145/3580305.3599389
Liu X(2023)Query Sub-intent Mining by Incorporating Search Results with Query Logs for Information Retrieval2023 IEEE 8th International Conference on Big Data Analytics (ICBDA)10.1109/ICBDA57405.2023.10104948(180-186)Online publication date: 3-Mar-2023
https://doi.org/10.1109/ICBDA57405.2023.10104948
Deng ZDou ZWen J(2023)DeepQFM: a deep learning based query facets mining methodInformation Retrieval10.1007/s10791-023-09427-026:1-2Online publication date: 30-Oct-2023
https://dl.acm.org/doi/10.1007/s10791-023-09427-0
Show More Cited By

Index Terms

Modeling reformulation using query distributions
1. Information systems
  1. Information retrieval

Recommendations

Modeling reformulation using passage analysis
CIKM '10: Proceedings of the 19th ACM international conference on Information and knowledge management

Query reformulation modifies the original query with the aim of better matching the vocabulary of the relevant documents, and consequently improving ranking effectiveness. Previous techniques typically generate words and phrases related to the original ...
Query reformulation using anchor text
WSDM '10: Proceedings of the third ACM international conference on Web search and data mining

Query reformulation techniques based on query logs have been studied as a method of capturing user intent and improving retrieval effectiveness. The evaluation of these techniques has primarily, however, focused on proprietary query logs and selected ...
Modeling reformulation as query distributions

Comments

Information & Contributors

Information

Published In

cover image ACM Transactions on Information Systems

ACM Transactions on Information Systems Volume 31, Issue 2

May 2013

180 pages

ISSN:1046-8188

EISSN:1558-2868

DOI:10.1145/2457465

Issue’s Table of Contents

Copyright © 2013 ACM.

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]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 17 May 2013

Accepted: 01 November 2012

Revised: 01 June 2012

Received: 01 November 2011

Published in TOIS Volume 31, Issue 2

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed

Funding Sources

Division of Information and Intelligent Systems
Center for Intelligent Information Retrieval

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

17
Total Citations
View Citations
609
Total Downloads

Downloads (Last 12 months)6
Downloads (Last 6 weeks)1

Reflects downloads up to 26 Jul 2024

Other Metrics

View Author Metrics

Citations

Cited By

Zhao ZDou ZGuo YCao ZCheng XSingh ASun YAkoglu LGunopulos DYan XKumar ROzcan FYe J(2023)Improving Search Clarification with Structured Information Extracted from Search ResultsProceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3580305.3599389(3549-3558)Online publication date: 6-Aug-2023
https://dl.acm.org/doi/10.1145/3580305.3599389
Liu X(2023)Query Sub-intent Mining by Incorporating Search Results with Query Logs for Information Retrieval2023 IEEE 8th International Conference on Big Data Analytics (ICBDA)10.1109/ICBDA57405.2023.10104948(180-186)Online publication date: 3-Mar-2023
https://doi.org/10.1109/ICBDA57405.2023.10104948
Deng ZDou ZWen J(2023)DeepQFM: a deep learning based query facets mining methodInformation Retrieval10.1007/s10791-023-09427-026:1-2Online publication date: 30-Oct-2023
https://dl.acm.org/doi/10.1007/s10791-023-09427-0
Samarinas CDharawat AZamani HCrestani FPasi GGaussier E(2022)Revisiting Open Domain Query Facet Extraction and GenerationProceedings of the 2022 ACM SIGIR International Conference on Theory of Information Retrieval10.1145/3539813.3545138(43-50)Online publication date: 23-Aug-2022
https://dl.acm.org/doi/10.1145/3539813.3545138
Hashemi HZamani HCroft WAl Hasan MXiong L(2022)Stochastic Optimization of Text Set Generation for Learning Multiple Query Intent RepresentationsProceedings of the 31st ACM International Conference on Information & Knowledge Management10.1145/3511808.3557666(4003-4008)Online publication date: 17-Oct-2022
https://dl.acm.org/doi/10.1145/3511808.3557666
Hashemi HZamani HCroft WDemartini GZuccon GCulpepper JHuang ZTong H(2021)Learning Multiple Intent Representations for Search QueriesProceedings of the 30th ACM International Conference on Information & Knowledge Management10.1145/3459637.3482445(669-679)Online publication date: 26-Oct-2021
https://dl.acm.org/doi/10.1145/3459637.3482445
Liu BLu XCulpepper J(2021)Strong natural language query generationInformation Retrieval Journal10.1007/s10791-021-09395-3Online publication date: 15-Jul-2021
https://doi.org/10.1007/s10791-021-09395-3
Raiber FKurland O(2019)Relevance FeedbackACM Transactions on Information Systems10.1145/336048737:4(1-28)Online publication date: 4-Oct-2019
https://dl.acm.org/doi/10.1145/3360487
Benham RMackenzie JMoffat ACulpepper J(2019)Boosting Search Performance Using Query VariationsACM Transactions on Information Systems10.1145/334500137:4(1-25)Online publication date: 4-Oct-2019
https://dl.acm.org/doi/10.1145/3345001
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
Show More Cited By

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Article

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Issue’s Table of Contents