short-paper

Cluster-Based Focused Retrieval

Authors:

Eilon Sheetrit,

Oren KurlandAuthors Info & Claims

CIKM '19: Proceedings of the 28th ACM International Conference on Information and Knowledge Management

Pages 2305 - 2308

https://doi.org/10.1145/3357384.3358087

Published: 03 November 2019 Publication History

Abstract

The focused retrieval task is to rank documents' passages by their presumed relevance to a query. Inspired by work on cluster-based document retrieval, we present a novel cluster-based focused retrieval method. The method is based on ranking clusters of similar passages using a learning-to-rank approach and transforming the cluster ranking to passage ranking. Empirical evaluation demonstrates the clear merits of the method.

References

[1]

Paavo Arvola, Shlomo Geva, Jaap Kamps, Ralf Schenkel, Andrew Trotman, and Johanna Vainio. 2011. Overview of the INEX 2010 ad hoc track. In Comparative Evaluation of Focused Retrieval. 1--32.

[2]

Michael Bendersky, W Bruce Croft, and Yanlei Diao. 2011. Quality-biased ranking of web documents. In Proc. of WSDM. 95--104.

Digital Library

[3]

David Buffoni, Nicolas Usunier, and Patrick Gallinari. 2010. Lip6 at INEX: OWPC for ad hoc track. In Focused Retrieval and Evaluation . 59--69.

[4]

James P. Callan. 1994. Passage-Level Evidence in Document Retrieval. In Proc. of SIGIR. 302--301.

Digital Library

[5]

Ruey-Cheng Chen, Evi Yulianti, Mark Sanderson, and W Bruce Croft. 2017. On the Benefit of Incorporating External Features in a Neural Architecture for Answer Sentence Selection. In Proc. of SIGIR. 1017--1020.

Digital Library

[6]

Daniel Cohen and W Bruce Croft. 2016. End to end long short term memory networks for non-factoid question answering. In Proc. of ICTIR. 143--146.

Digital Library

[7]

Shlomo Geva, Jaap Kamps, Miro Lethonen, Ralf Schenkel, James A Thom, and Andrew Trotman. 2010. Overview of the INEX 2009 ad hoc track. In Focused retrieval and evaluation . 4--25.

[8]

Nick Jardine and C. J. van Rijsbergen. 1971. The use of hierarchic clustering in information retrieval. Information storage and retrieval, Vol. 7, 5 (1971), 217--240.

[9]

Thorsten Joachims. 2006. Training linear SVMs in linear time. In Proc. of KDD. 217--226.

Digital Library

[10]

Oren Kurland. 2009. Re-ranking search results using language models of query-specific clusters. Information Retrieval, Vol. 12, 4 (2009), 437--460.

Digital Library

[11]

Oren Kurland and Carmel Domshlak. 2008. A rank-aggregation approach to searching for optimal query-specific clusters. In Proc. of SIGIR. 547--554.

Digital Library

[12]

Oren Kurland and Eyal Krikon. 2011. The opposite of smoothing: a language model approach to ranking query-specific document clusters. Journal of Artificial Intelligence Research, Vol. 41 (2011), 367--395.

Digital Library

[13]

Oren Kurland and Lillian Lee. 2004. Corpus structure, language models, and ad hoc information retrieval. In Proc. of SIGIR . 194--201.

Digital Library

[14]

Xiaoyong Liu and W Bruce Croft. 2004. Cluster-based retrieval using language models. In Proc. of SIGIR. 186--193.

Digital Library

[15]

Xiaoyong Liu and W Bruce Croft. 2006. Experiments on retrieval of optimal clusters . Technical Report. Technical Report IR-478, Center for Intelligent Information Retrieval (CIIR), University of Massachusetts.

[16]

Xiaoyong Liu and W Bruce Croft. 2008. Evaluating text representations for retrieval of the best group of documents. In Proc. of ECIR . 454--462.

[17]

Vanessa Graham Murdock. 2006. Aspects of sentence retrieval . Ph.D. Dissertation. University of Massachusetts Amherst.

[18]

Fiana Raiber and Oren Kurland. 2013. Ranking document clusters using markov random fields. In Proc. of SIGIR . 333--342.

Digital Library

[19]

Tetsuya Sakai, Toshihiko Manabe, and Makoto Koyama. 2005. Flexible pseudo-relevance feedback via selective sampling. TALIP, Vol. 4, 2 (2005), 111--135.

Digital Library

[20]

Gerard Salton, James Allan, and Chris Buckley. 1993. Approaches to passage retrieval in full text information systems. In Proc. of SIGIR . 49--58.

Digital Library

[21]

Aliaksei Severyn and Alessandro Moschitti. 2015. Learning to rank short text pairs with convolutional deep neural networks. In Proc. of SIGIR. 373--382.

Digital Library

[22]

Eilon Sheetrit, Anna Shtok, Oren Kurland, and Igal Shprincis. 2018. Testing the Cluster Hypothesis with Focused and Graded Relevance Judgments. In Proc. of SIGIR . 1173--1176.

Digital Library

[23]

Ian Soboroff. 2004. Overview of the TREC 2004 Novelty Track. In Proc. of TREC .

[24]

Ian Soboroff and Donna Harman. 2003. Overview of the TREC 2003 Novelty Track. In Proc. of TREC. 38--53.

[25]

Anastasios Tombros, Robert Villa, and C. J. Van Rijsbergen. 2002. The effectiveness of query-specific hierarchic clustering in information retrieval. Information processing & management, Vol. 38, 4 (2002), 559--582.

[26]

Ellen M. Voorhees. 1985. The cluster hypothesis revisited. In Proc. of SIGIR. 188--196.

Digital Library

[27]

Liu Yang, Qingyao Ai, Jiafeng Guo, and W Bruce Croft. 2016a. aNMM: Ranking short answer texts with attention-based neural matching model. In Proc. of CIKM. 287--296.

Digital Library

[28]

Liu Yang, Qingyao Ai, Damiano Spina, Ruey-Cheng Chen, Liang Pang, W Bruce Croft, Jiafeng Guo, and Falk Scholer. 2016b. Beyond factoid QA: Effective methods for non-factoid answer sentence retrieval. In Proc. of ECIR. 115--128.

[29]

Chengxiang Zhai and John Lafferty. 2001. A study of smoothing methods for language models applied to ad hoc information retrieval. In Proc. of SIGIR. 334--342.

Digital Library

Cited By

Tian SYan F(2024)A Review of Research into the Standardization of High-Speed Railway Text Operation and Maintenance DataProceedings of the 6th International Conference on Electrical Engineering and Information Technologies for Rail Transportation (EITRT) 202310.1007/978-981-99-9315-4_23(213-221)Online publication date: 9-Jan-2024
https://doi.org/10.1007/978-981-99-9315-4_23
Shabina Chawla S(2022)Collaborative Approach Toward Information Retrieval System to Get Relevant News Articles Over Web: IRS-WebComputational Intelligence and Data Analytics10.1007/978-981-19-3391-2_35(461-474)Online publication date: 2-Sep-2022
https://doi.org/10.1007/978-981-19-3391-2_35
Sarwar GO'Riordan C(2021)Passage Based Answer-Set Graph Approach for Query Performance PredictionProceedings of the 25th Australasian Document Computing Symposium10.1145/3503516.3503534(1-6)Online publication date: 9-Dec-2021
https://dl.acm.org/doi/10.1145/3503516.3503534
Show More Cited By

Index Terms

Cluster-Based Focused Retrieval
1. Information systems
  1. Information retrieval
    1. Retrieval models and ranking
      1. Learning to rank
      2. Similarity measures
    2. Retrieval tasks and goals
      1. Clustering and classification

Recommendations

From Cluster Ranking to Document Ranking
SIGIR '22: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval

The common approach of using clusters of similar documents for ad hoc document retrieval is to rank the clusters in response to the query; then, the cluster ranking is transformed to document ranking. We present a novel supervised approach to transform ...
Ranking document clusters using markov random fields
SIGIR '13: Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval

An important challenge in cluster-based document retrieval is ranking document clusters by their relevance to the query. We present a novel cluster ranking approach that utilizes Markov Random Fields (MRFs). MRFs enable the integration of various types ...
Enhancing relevance models with adaptive passage retrieval
ECIR'08: Proceedings of the IR research, 30th European conference on Advances in information retrieval

Passage retrieval and pseudo relevance feedback/query expansion have been reported as two effective means for improving document retrieval in literature. Relevance models, while improving retrieval in most cases, hurts performance on some heterogeneous ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

CIKM '19: Proceedings of the 28th ACM International Conference on Information and Knowledge Management

November 2019

3373 pages

ISBN:9781450369763

DOI:10.1145/3357384

General Chairs:
Wenwu Zhu
Tsinghua University, China
,
Dacheng Tao
University of Massachusetts, USA
,
Xueqi Cheng
Institute of Computing Technology, CAS, China
,
Program Chairs:
Peng Cui
Tsinghua University, China
,
Elke Rundensteiner
Worcester Polytechnic Institute, USA
,
David Carmel
Amazon Research, USA
,
Qi He
LinkedIn, USA
,
Jeffrey Xu Yu
Chinese University of Hong Kong, China

Copyright © 2019 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]

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 03 November 2019

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Short-paper

Funding Sources

German Research Foundation (DFG) via the German-Israeli Project Cooperation (DIP)

Conference

CIKM '19

Sponsor:

CIKM '19: The 28th ACM International Conference on Information and Knowledge Management

November 3 - 7, 2019

Beijing, China

Acceptance Rates

CIKM '19 Paper Acceptance Rate 202 of 1,031 submissions, 20%;

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

Upcoming Conference

CIKM '25

Sponsor:
sigir
sigir

The 34th ACM International Conference on Information and Knowledge Management

November 10 - 14, 2025

Seoul , Republic of Korea

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

5
Total Citations
View Citations
212
Total Downloads

Downloads (Last 12 months)5
Downloads (Last 6 weeks)0

Reflects downloads up to 01 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

Tian SYan F(2024)A Review of Research into the Standardization of High-Speed Railway Text Operation and Maintenance DataProceedings of the 6th International Conference on Electrical Engineering and Information Technologies for Rail Transportation (EITRT) 202310.1007/978-981-99-9315-4_23(213-221)Online publication date: 9-Jan-2024
https://doi.org/10.1007/978-981-99-9315-4_23
Shabina Chawla S(2022)Collaborative Approach Toward Information Retrieval System to Get Relevant News Articles Over Web: IRS-WebComputational Intelligence and Data Analytics10.1007/978-981-19-3391-2_35(461-474)Online publication date: 2-Sep-2022
https://doi.org/10.1007/978-981-19-3391-2_35
Sarwar GO'Riordan C(2021)Passage Based Answer-Set Graph Approach for Query Performance PredictionProceedings of the 25th Australasian Document Computing Symposium10.1145/3503516.3503534(1-6)Online publication date: 9-Dec-2021
https://dl.acm.org/doi/10.1145/3503516.3503534
Sheetrit EShtok AKurland O(2020)A passage-based approach to learning to rank documentsInformation Retrieval Journal10.1007/s10791-020-09369-x23:2(159-186)Online publication date: 6-Mar-2020
https://doi.org/10.1007/s10791-020-09369-x
Djenouri YBelhadi ADjenouri DLin J(2020)Cluster-based information retrieval using pattern miningApplied Intelligence10.1007/s10489-020-01922-xOnline publication date: 17-Oct-2020
https://doi.org/10.1007/s10489-020-01922-x

View Options

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 Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents