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A reranking model for genomics aspect search

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

Pages 783 - 784

https://doi.org/10.1145/1390334.1390502

Published: 20 July 2008 Publication History

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Abstract

In this paper, we propose a reranking model to improve the aspect-level performance in the biomedical domain. This model iteratively computes the maximum hidden aspect for every retrieved passage and then reranks these passages from aspect subsets. The experimental results show the improvements of the aspect-level performance up to 27.14% for 2006 Genomics topics and 27.09% for 2007 Genomics topics.

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Andrew B. Goldberg et al. (2006) Ranking Biomedical Passages for Relevance and Diversity. In Proc. of TREC-15, NIST Special Publication, Nov. 2006.

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Cited By

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An XCercone N(2014)How Complementary Are Different Information Retrieval Techniques? A Study in Biomedicine DomainProceedings of the 15th International Conference on Computational Linguistics and Intelligent Text Processing - Volume 840410.1007/978-3-642-54903-8_31(367-380)Online publication date: 6-Apr-2014
https://dl.acm.org/doi/10.1007/978-3-642-54903-8_31
An XHuang JJones GSheridan PKelly Dde Rijke MSakai T(2013)Boosting novelty for biomedical information retrieval through probabilistic latent semantic analysisProceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval10.1145/2484028.2484174(829-832)Online publication date: 28-Jul-2013
https://dl.acm.org/doi/10.1145/2484028.2484174
An XHuang XCercone a(2010)Optimal IRProceedings of the 11th international conference on Computational Linguistics and Intelligent Text Processing10.1007/978-3-642-12116-6_51(602-613)Online publication date: 21-Mar-2010
https://dl.acm.org/doi/10.1007/978-3-642-12116-6_51

Index Terms

A reranking model for genomics aspect search
1. Information systems
  1. Information retrieval

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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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An XCercone N(2014)How Complementary Are Different Information Retrieval Techniques? A Study in Biomedicine DomainProceedings of the 15th International Conference on Computational Linguistics and Intelligent Text Processing - Volume 840410.1007/978-3-642-54903-8_31(367-380)Online publication date: 6-Apr-2014
https://dl.acm.org/doi/10.1007/978-3-642-54903-8_31
An XHuang JJones GSheridan PKelly Dde Rijke MSakai T(2013)Boosting novelty for biomedical information retrieval through probabilistic latent semantic analysisProceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval10.1145/2484028.2484174(829-832)Online publication date: 28-Jul-2013
https://dl.acm.org/doi/10.1145/2484028.2484174
An XHuang XCercone a(2010)Optimal IRProceedings of the 11th international conference on Computational Linguistics and Intelligent Text Processing10.1007/978-3-642-12116-6_51(602-613)Online publication date: 21-Mar-2010
https://dl.acm.org/doi/10.1007/978-3-642-12116-6_51

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Boosting novelty for biomedical information retrieval through probabilistic latent semantic analysis

Optimizing video search reranking via minimum incremental information loss

Reranking search results for sparse queries