research-article

Geometric representations for multiple documents

Authors:

W. Bruce CroftAuthors Info & Claims

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

Pages 251 - 258

https://doi.org/10.1145/1835449.1835493

Published: 19 July 2010 Publication History

Abstract

Combining multiple documents to represent an information object is well-known as an effective approach for many Information Retrieval tasks. For example, passages can be combined to represent a document for retrieval, document clusters are represented using combinations of the documents they contain, and feedback documents can be combined to represent a query model. Various techniques for combination have been introduced, and among them, representation techniques based on concatenation and the arithmetic mean are frequently used. Some recent work has shown the potential of a new representation technique using the geometric mean. However, these studies lack a theoretical foundation explaining why the geometric mean should have advantages for representing multiple documents. In this paper, we show that the arithmetic mean and the geometric mean are approximations to the center of mass in certain geometries, and show empirically that the geometric mean is closer to the center. Through experiments with two IR tasks, we show the potential benefits for geometric representations, including a geometry-based pseudo-relevance feedback method that outperforms state-of-the-art techniques.

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Cited By

Huang RSong SLee YPark JKim SYi S(2020)Effective and efficient retrieval of structured entitiesProceedings of the VLDB Endowment10.14778/3380750.338075413:6(826-839)Online publication date: 11-Mar-2020
https://dl.acm.org/doi/10.14778/3380750.3380754
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
Valcarce DParapar JBarreiro Á(2019)Document-based and term-based linear methods for pseudo-relevance feedbackACM SIGAPP Applied Computing Review10.1145/3307624.330762618:4(5-17)Online publication date: 15-Jan-2019
https://dl.acm.org/doi/10.1145/3307624.3307626
Show More Cited By

Index Terms

Geometric representations for multiple documents
1. Information systems
  1. Information retrieval
    1. Retrieval models and ranking

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Published In

cover image ACM Conferences

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

Copyright © 2010 ACM.

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SIGIR: ACM Special Interest Group on Information Retrieval

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 19 July 2010

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SIGIR '10

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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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Overall Acceptance Rate 705 of 3,463 submissions, 20%

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Cited By

Huang RSong SLee YPark JKim SYi S(2020)Effective and efficient retrieval of structured entitiesProceedings of the VLDB Endowment10.14778/3380750.338075413:6(826-839)Online publication date: 11-Mar-2020
https://dl.acm.org/doi/10.14778/3380750.3380754
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
Valcarce DParapar JBarreiro Á(2019)Document-based and term-based linear methods for pseudo-relevance feedbackACM SIGAPP Applied Computing Review10.1145/3307624.330762618:4(5-17)Online publication date: 15-Jan-2019
https://dl.acm.org/doi/10.1145/3307624.3307626
Pengming WQing CBin W(2019)Temporal Smoothing: Discriminatively Incorporating Various Temporal Profiles of QueriesInformation Retrieval10.1007/978-3-030-31624-2_3(26-38)Online publication date: 20-Sep-2019
https://dl.acm.org/doi/10.1007/978-3-030-31624-2_3
Montazeralghaem AZamani HShakery ACollins-Thompson KMei QDavison BLiu YYilmaz E(2018)Theoretical Analysis of Interdependent Constraints in Pseudo-Relevance FeedbackThe 41st International ACM SIGIR Conference on Research & Development in Information Retrieval10.1145/3209978.3210156(1249-1252)Online publication date: 27-Jun-2018
https://dl.acm.org/doi/10.1145/3209978.3210156
Valcarce DParapar JBarreiro ÁHaddad HWainwright RChbeir R(2018)LiMeProceedings of the 33rd Annual ACM Symposium on Applied Computing10.1145/3167132.3167207(678-687)Online publication date: 9-Apr-2018
https://dl.acm.org/doi/10.1145/3167132.3167207
Ariannezhad MMontazeralghaem AZamani HShakery A(2017)Iterative Estimation of Document Relevance Score for Pseudo-Relevance FeedbackAdvances in Information Retrieval10.1007/978-3-319-56608-5_65(676-683)Online publication date: 8-Apr-2017
https://doi.org/10.1007/978-3-319-56608-5_65
Kuzi SShtok AKurland OCarterette BFang HLalmas MNie J(2016)Query Anchoring Using Discriminative Query ModelsProceedings of the 2016 ACM International Conference on the Theory of Information Retrieval10.1145/2970398.2970402(219-228)Online publication date: 12-Sep-2016
https://dl.acm.org/doi/10.1145/2970398.2970402
Montazeralghaem AZamani HShakery APerego RSebastiani FAslam JRuthven IZobel J(2016)Axiomatic Analysis for Improving the Log-Logistic Feedback ModelProceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval10.1145/2911451.2914768(765-768)Online publication date: 7-Jul-2016
https://dl.acm.org/doi/10.1145/2911451.2914768
Pal DMitra MBhattacharya SAllan JCroft Bde Vries AZhai C(2015)Improving Pseudo Relevance Feedback in the Divergence from Randomness ModelProceedings of the 2015 International Conference on The Theory of Information Retrieval10.1145/2808194.2809494(325-328)Online publication date: 27-Sep-2015
https://dl.acm.org/doi/10.1145/2808194.2809494
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