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Discretization based learning approach to information retrieval

Published: 31 October 2005 Publication History

Abstract

We have designed a representation scheme, which is based on the discrete representation of a document ranking function, which is capable of reproducing and enhancing the properties of such popular ranking functions as tf.idf, BM25 or those based on language models. Our tests have demonstrated the capability of our approach to achieve the performance of the best known scoring functions solely through training, without using any known heuristic or analytic formulas.

References

[1]
Joachims, T. (2001). A Statistical Learning Model of Text Classification with Support Vector Machines. Proceedings of the Conference on Research and Development in Information Retrieval (SIGIR), 2001.
[2]
Kraaij, W., Westerveld T. and Hiemstra, D. (2003). The Lemur Toolkit for Language Modeling and Information Retrieval, http://www-2.cs.cmu.edu/~lemur
[3]
Roussinov, D., and Fan, W., Discretization Based Learning Approach to Information Retrieval. In proceedings of 2005 Conference on Human Language Technologies (to appear).

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  1. Discretization based learning approach to information retrieval

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    cover image ACM Conferences
    CIKM '05: Proceedings of the 14th ACM international conference on Information and knowledge management
    October 2005
    854 pages
    ISBN:1595931406
    DOI:10.1145/1099554
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    Publication History

    Published: 31 October 2005

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    CIKM05: Conference on Information and Knowledge Management
    October 31 - November 5, 2005
    Bremen, Germany

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    CIKM '05 Paper Acceptance Rate 77 of 425 submissions, 18%;
    Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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