Article

A dual index model for contextual information retrieval

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SIGIR '05: Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval

Pages 613 - 614

https://doi.org/10.1145/1076034.1076154

Published: 15 August 2005 Publication History

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Abstract

In this paper, we propose a dual index model for contextual IR. For each query, we search against both document level and passage level indexes, and use the corresponding merge function to update the weights for both documents and paragraphs by combining the results from both indexes according to the granularity information in metadata. Experiments on 2004 TREC data show that a significant improvement can be made by using the dual index model.

References

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N. Abdul-Jaleel, J. Allan, W. Bruce and et al. UMass at TREC 2004: Notebook. In Proceedings of TREC-13. NIST Special Publication, 2004.

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J. Allan. HARD Track Overview in TREC 2004. In Proc. of TREC-13. NIST Special Publication, 2004.

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J. Allan and et al. Challenges in IR and Language Modeling. SIGIR Forum, 37(1):31--47, 2003.

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X. Huang, Y. Huang and M. Wen et al. York University at TREC 2004: HARD and Genomics Tracks. In Proc. of TREC-13. NIST Special Publication, 2004.

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D. Harper and et al. The Robert Gordon Univ.'s HARD Track Experiments at TREC 2004. In Proceedings of TREC-13. NIST Special Publication, 2004.

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J. Jiang and C.X.Zhai. UIUC in HARD 2004: Passage Retrieval Using HMMs. In Proceedings of TREC-13. NIST Special Publication, 2004.

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Cited By

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Hu QHuang J(2018)Passage extraction and result combination for genomics information retrievalJournal of Intelligent Information Systems10.1007/s10844-009-0097-434:3(249-274)Online publication date: 28-Dec-2018
https://dl.acm.org/doi/10.1007/s10844-009-0097-4
Ye ZHuang JLin H(2011)Incorporating rich features to boost information retrieval performanceExpert Systems with Applications: An International Journal10.1016/j.eswa.2010.12.10838:6(7569-7574)Online publication date: 1-Jun-2011
https://dl.acm.org/doi/10.1016/j.eswa.2010.12.108
Hu QHuang XZhang ABorodovsky MOzsoyoglu GMikler A(2010)Genomics information retrieval using a Bayesian model for learning and re-rankingProceedings of the First ACM International Conference on Bioinformatics and Computational Biology10.1145/1854776.1854846(426-429)Online publication date: 2-Aug-2010
https://dl.acm.org/doi/10.1145/1854776.1854846
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Index Terms

A dual index model for contextual information retrieval
1. Information systems
  1. Information retrieval

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Published In

SIGIR '05: Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval

August 2005

708 pages

ISBN:1595930345

DOI:10.1145/1076034

General Chairs:
Ricardo Baeza-Yates
University of Chile, Chile
,
Nivio Ziviani
Federal University of Minas Gerais, Brazil
,
Program Chairs:
Gary Marchionini
University of North Carolina, USA
,
Alistair Moffat
University of Melbourne, Australia
,
John Tait
University of Sunderland, UK

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]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 15 August 2005

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SIGIR05

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SIGIR

SIGIR05: The 28th ACM/SIGIR International Symposium on Information Retrieval 2005

August 15 - 19, 2005

Salvador, Brazil

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

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Cited By

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Hu QHuang J(2018)Passage extraction and result combination for genomics information retrievalJournal of Intelligent Information Systems10.1007/s10844-009-0097-434:3(249-274)Online publication date: 28-Dec-2018
https://dl.acm.org/doi/10.1007/s10844-009-0097-4
Ye ZHuang JLin H(2011)Incorporating rich features to boost information retrieval performanceExpert Systems with Applications: An International Journal10.1016/j.eswa.2010.12.10838:6(7569-7574)Online publication date: 1-Jun-2011
https://dl.acm.org/doi/10.1016/j.eswa.2010.12.108
Hu QHuang XZhang ABorodovsky MOzsoyoglu GMikler A(2010)Genomics information retrieval using a Bayesian model for learning and re-rankingProceedings of the First ACM International Conference on Bioinformatics and Computational Biology10.1145/1854776.1854846(426-429)Online publication date: 2-Aug-2010
https://dl.acm.org/doi/10.1145/1854776.1854846
Huang XWen MAn AHuang YEfthimiadis EDumais SHawking DJärvelin, K(2006)A platform for Okapi-based contextual information retrievalProceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval10.1145/1148170.1148341(728-728)Online publication date: 6-Aug-2006
https://dl.acm.org/doi/10.1145/1148170.1148341
Miao Wen Xiangji Huang (2006)A multilevel searching and re-ranking framework for information retrieval2006 IEEE International Conference on Granular Computing10.1109/GRC.2006.1635879(619-622)Online publication date: 2006
https://doi.org/10.1109/GRC.2006.1635879

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Evaluating high accuracy retrieval techniques

Contextual relevance feedback in web information retrieval

Retrieval effectiveness of an ontology-based model for information selection