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Statistical significance of MUC-6 results

Published: 06 November 1995 Publication History
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  • Abstract

    The results of the MUC-6 evaluation must be analyzed to determine whether close scores significantly distinguish systems or whether the differences in those scores are a matter of chance. In order to do such an analysis, a method of computer intensive hypothesis testing was developed by SAIC for the MUC-3 results and has been used for distinguishing MUC scores since that time. The implementation of this method for the MUC evaluations was first described in [1] and later the concepts behind the statistical model were explained in a more understandable manner in [2]. This paper gives the results of the statistical testing for the three MUC-6 tasks where a single metric could be associated with a system's performance.

    References

    [1]
    Chinchor, N., Hirschman, L., and D. Lewis (1993) "Evaluating Message Understanding Systems: An Analysis of the Third Message Understanding Conference (MUC-3)" Computational Linguistics 19(3).
    [2]
    Chinchor, N. (1992). "The Statistical Significance of the MUC-4 Results" Proceedings of the Fourth Message Understanding Conference (MUC-4). Morgan Kaufmann, Publishers. San Mateo, CA.
    [3]
    Noreen, W. (1989) Computer Intensive Methods for Testing Hypotheses: An Introduction. John Wiley & Sons.
    [4]
    Van Rijsbergen, C. J. (1979) Information Retrieval. London: Butterworths.

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    • (2009)Learning document-level semantic properties from free-text annotationsJournal of Artificial Intelligence Research10.5555/1622716.162273134:1(569-603)Online publication date: 1-Apr-2009
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    Published In

    cover image DL Hosted proceedings
    MUC6 '95: Proceedings of the 6th conference on Message understanding
    November 1995
    353 pages
    ISBN:1558604022

    Publisher

    Association for Computational Linguistics

    United States

    Publication History

    Published: 06 November 1995

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    • (2010)Maximum metric score training for coreference resolutionProceedings of the 23rd International Conference on Computational Linguistics10.5555/1873781.1873928(1308-1316)Online publication date: 23-Aug-2010
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    • (2009)Learning document-level semantic properties from free-text annotationsJournal of Artificial Intelligence Research10.5555/1622716.162273134:1(569-603)Online publication date: 1-Apr-2009
    • (2009)Global models of document structure using latent permutationsProceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics10.5555/1620754.1620808(371-379)Online publication date: 31-May-2009
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    • (2002)Named entity recognition using an HMM-based chunk taggerProceedings of the 40th Annual Meeting on Association for Computational Linguistics10.3115/1073083.1073163(473-480)Online publication date: 6-Jul-2002
    • (2000)An empirically based system for processing definite descriptionsComputational Linguistics10.1162/08912010075010594826:4(539-593)Online publication date: 1-Dec-2000
    • (1999)An Algorithm that Learns What‘s in a NameMachine Language10.1023/A:100755822112234:1-3(211-231)Online publication date: 1-Feb-1999

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