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Re-ranking search results using document-passage graphs

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SIGIR '08: Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval

Pages 853 - 854

https://doi.org/10.1145/1390334.1390539

Published: 20 July 2008 Publication History

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Abstract

We present a novel passage-based approach to re-ranking documents in an initially retrieved list so as to improve precision at top ranks. While most work on passage-based document retrieval ranks a document based on the query similarity of its constituent passages, our approach leverages information about the centrality of the document passages with respect to the initial document list. Passage centrality is induced over a bipartite document-passage graph, wherein edge weights represent document-passage similarities. Empirical evaluation shows that our approach yields effective re-ranking performance. Furthermore, the performance is superior to that of previously proposed passage-based document ranking methods.

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Cited By

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Su ZDou ZZhu YWen J(2024)Passage-aware Search Result DiversificationACM Transactions on Information Systems10.1145/365367242:5(1-29)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3653672
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
Sarwar GO’Riordan CNewell J(2019)Investigation of Passage Based Ranking Models to Improve Document RetrievalKnowledge Discovery, Knowledge Engineering and Knowledge Management10.1007/978-3-030-15640-4_6(100-117)Online publication date: 15-Mar-2019
https://doi.org/10.1007/978-3-030-15640-4_6
Show More Cited By

Index Terms

Re-ranking search results using document-passage graphs
1. Information systems
  1. Information retrieval
    1. Retrieval models and ranking

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Published In

SIGIR '08: Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval

July 2008

934 pages

ISBN:9781605581644

DOI:10.1145/1390334

General Chairs:
Tat-Seng Chua
National University of Singapore
,
Mun-Kew Leong
National Library Board, Singapore
,
Program Chairs:
Syung Hyon Myaeng
Information and Communications University, Korea
,
Douglas W. Oard
University of Maryland, College Park, USA
,
Fabrizio Sebastiani
Consiglio Nazionale delle Ricerche, Italy

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 20 July 2008

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SIGIR '08

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SIGIR '08: The 31st Annual International ACM SIGIR Conference

July 20 - 24, 2008

Singapore, Singapore

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

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Cited By

View all

Su ZDou ZZhu YWen J(2024)Passage-aware Search Result DiversificationACM Transactions on Information Systems10.1145/365367242:5(1-29)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3653672
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
Sarwar GO’Riordan CNewell J(2019)Investigation of Passage Based Ranking Models to Improve Document RetrievalKnowledge Discovery, Knowledge Engineering and Knowledge Management10.1007/978-3-030-15640-4_6(100-117)Online publication date: 15-Mar-2019
https://doi.org/10.1007/978-3-030-15640-4_6
Mei TRui YLi STian Q(2014)Multimedia search rerankingACM Computing Surveys10.1145/253679846:3(1-38)Online publication date: 1-Jan-2014
https://dl.acm.org/doi/10.1145/2536798
Kurland O(2014)The Cluster Hypothesis in Information RetrievalProceedings of the 36th European Conference on IR Research on Advances in Information Retrieval - Volume 841610.1007/978-3-319-06028-6_105(823-826)Online publication date: 13-Apr-2014
https://dl.acm.org/doi/10.1007/978-3-319-06028-6_105
Kong WAktolga EAllan JHe QIyengar ANejdl WPei JRastogi R(2013)Improving passage ranking with user behavior informationProceedings of the 22nd ACM international conference on Information & Knowledge Management10.1145/2505515.2505719(1999-2008)Online publication date: 27-Oct-2013
https://dl.acm.org/doi/10.1145/2505515.2505719
Zhang JMinami KKawai YShiraishi YKumamoto T(2013)Personalized Web Search Using Emotional FeaturesAvailability, Reliability, and Security in Information Systems and HCI10.1007/978-3-642-40511-2_6(69-83)Online publication date: 2013
https://doi.org/10.1007/978-3-642-40511-2_6
Villatoro EJuárez AMontes MVillaseñor LSucar L(2012)Document ranking refinement using a markov random field model*Natural Language Engineering10.1017/S135132491200001018:2(155-185)Online publication date: 1-Mar-2012
https://dl.acm.org/doi/10.1017/S1351324912000010
Tsai FChan K(2011)An intelligent system for sentence retrieval and novelty miningInternational Journal of Knowledge Engineering and Data Mining10.1504/IJKEDM.2011.0376451:3(235-253)Online publication date: 1-Dec-2011
https://dl.acm.org/doi/10.1504/IJKEDM.2011.037645
Krikon EKurland OBendersky M(2010)Utilizing inter-passage and inter-document similarities for reranking search resultsACM Transactions on Information Systems10.1145/1877766.187776929:1(1-28)Online publication date: 27-Dec-2010
https://dl.acm.org/doi/10.1145/1877766.1877769
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