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An incremental decision list learner

Published: 06 July 2002 Publication History

Abstract

We demonstrate a problem with the standard technique for learning probabilistic decision lists. We describe a simple, incremental algorithm that avoids this problem, and show how to implement it efficiently. We also show a variation that adds thresholding to the standard sorting algorithm for decision lists, leading to similar improvements. Experimental results show that the new algorithm produces substantially lower error rates and entropy, while simultaneously learning lists that are over an order of magnitude smaller than those produced by the standard algorithm.

References

[1]
M. Banko and E. Brill. 2001a. Mitigating the paucity of data problem. In HLT.
[2]
M. Banko and E. Brill. 2001b. Scaling to very very large corpora for natural language disambiguation. In ACL.
[3]
E. Brill. 1995. Transformation-based error-driven learning and natural language processing: A case study in part-of-speech tagging. Comp. Ling., 21(4):543--565.
[4]
Stanley F. Chen and Joshua Goodman. 1999. An empirical study of smoothing techniques for language modeling. Computer Speech and Language, 13:359--394.
[5]
S.F. Chen and R. Rosenfeld. 1999. A gaussian prior for smoothing maximum entropy models. Technical Report CMU-CS-99-108, Computer Science Department, Carnegie Mellon University.
[6]
R. Florian, J. C. Henderson, and G. Ngai. 2000. Coaxing confidences out of an old friend: Probabilistic classifications from transformation rule lists. In EMNLP.
[7]
M. Kearns and R. Schapire. 1994. Efficient distribution-free learning of probabilistic concepts. Computer and System Sciences, 48(3):464--497.
[8]
W. Krauth and M. Mezard. 1987. Learning algorithms with optimal stability in neural networks. Journal of Physics A, 20:745--752.
[9]
R.J. Mooney and M. E. Califf. 1995. Induction of first-order decision lists: Results on learning the past tense of English verbs. In International Workshop on Inductive Logic Programming, pages 145--146.
[10]
G. Ngai and R. Florian. 2001. Transformation-based learning in the fast lane. In NA-ACL, pages 40--47.
[11]
L. Ramshaw and M. Marcus. 1994. Exploring the statistical derivation of transformational rule sequences for part-of-speech tagging. In Proceedings of the Balancing Act Workshop on Combining Symbolic and Statistical Approaches to Language, pages 86--95. ACL.
[12]
R. Rivest. 1987. Learning decision lists. Machine Learning, 2(3):229--246.
[13]
Dan Roth. 1998. Learning to resolve natural language ambiguities: A unified approach. In AAAI-98.
[14]
G. Webb. 1994. Learning decision lists by prepending inferred rules, vol. b. In Second Singapore International Conference on Intelligent Systems, pages 280--285.
[15]
David Yarowsky. 1994. Decision lists for lexical ambiguity resolution: Application to accent restoration in spanish and french. In ACL, pages 88--95.
[16]
David Yarowsky. 2000. Hierarchical decision lists for word sense disambiguation. Computers and the Humanities, 34(2):179--186.
[17]
Hugo Zaragoza and Ralf Herbrich. 2000. The perceptron meets reuters. In Workshop on Machine Learning for Text and Images at NIPS 2001.

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  • (2008)A Japanese predicate argument structure analysis using decision listsProceedings of the Conference on Empirical Methods in Natural Language Processing10.5555/1613715.1613780(523-532)Online publication date: 25-Oct-2008
  1. An incremental decision list learner

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    EMNLP '02: Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
    July 2002
    328 pages

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    Association for Computational Linguistics

    United States

    Publication History

    Published: 06 July 2002

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    • (2008)A Japanese predicate argument structure analysis using decision listsProceedings of the Conference on Empirical Methods in Natural Language Processing10.5555/1613715.1613780(523-532)Online publication date: 25-Oct-2008

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