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Combining heterogeneous classifiers for word-sense disambiguation

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Christopher D. ManningAuthors Info & Claims

WSD '02: Proceedings of the ACL-02 workshop on Word sense disambiguation: recent successes and future directions - Volume 8

Pages 74 - 80

https://doi.org/10.3115/1118675.1118686

Published: 11 July 2002 Publication History

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Abstract

This paper discusses ensembles of simple but heterogeneous classifiers for word-sense disambiguation, examining the Stanford-CS224N system entered in the SENSEVAL-2 English lexical sample task. First-order classifiers are combined by a second-order classifier, which variously uses majority voting, weighted voting, or a maximum entropy model. While individual first-order classifiers perform comparably to middle-scoring teams' systems, the combination achieves high performance. We discuss trade-offs and empirical performance. Finally, we present an analysis of the combination, examining how ensemble performance depends on error independence and task difficulty.

References

[1]

Alan Agresti. 1990. Categorical Data Analysis. John Wiley & Sons.

Google Scholar

[2]

Eric Bauer and Ron Kohavi. 1999. An empirical comparison of voting classication algorithms: Bagging, boosting and variants. Machine Learning, 36:105--139.

Digital Library

Google Scholar

[3]

Adam L. Berger, Stephen A. Della Pietra, and Vincent J. Della Pietra. 1996. A maximum entropy approach to natural language processing. Computational Linguistics, 22(1):39--71.

Digital Library

Google Scholar

[4]

Leo Breiman. 1996. Bagging predictors. Machine Learning, 24:123--140.

Crossref

Google Scholar

[5]

Raymond J. Mooney. 1996. Comparative experiments on disambiguating word senses: An illustration of the role of bias in machine learning. In EMNLP 1.

Google Scholar

[6]

Senseval-2. 2001. Senseval-2 proceedings, in publication.

Google Scholar

[7]

Kagan Tumer and Joydeep Ghosh. 1996. Error correlation and error reduction in ensemble classifiers. Connection Science, 8:385--404.

Crossref

Google Scholar

[8]

Xiru Zhang, Jill Mesirov, and David L. Waltz. 1992. Hybrid system for protein structure prediction. Journal of Molecular Biology, 225:1049--1063.

Crossref

Google Scholar

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Luong KHadi MThung FFard FLo DRastogi ATufano RBavota GArnaoudova VHaiduc S(2022)ARSeekProceedings of the 30th IEEE/ACM International Conference on Program Comprehension10.1145/3524610.3527918(331-342)Online publication date: 16-May-2022
https://dl.acm.org/doi/10.1145/3524610.3527918
Horowitz DKamvar S(2012)Searching the villageCommunications of the ACM10.1145/2133806.213383055:4(111-118)Online publication date: 1-Apr-2012
https://dl.acm.org/doi/10.1145/2133806.2133830
Horowitz DKamvar SRappa MJones PFreire JChakrabarti S(2010)The anatomy of a large-scale social search engineProceedings of the 19th international conference on World wide web10.1145/1772690.1772735(431-440)Online publication date: 26-Apr-2010
https://dl.acm.org/doi/10.1145/1772690.1772735
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Combining heterogeneous classifiers for word-sense disambiguation
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning
    2. Machine learning approaches
2. Hardware
  1. Power and energy
    1. Power estimation and optimization

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WSD '02: Proceedings of the ACL-02 workshop on Word sense disambiguation: recent successes and future directions - Volume 8

July 2002

123 pages

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Association for Computational Linguistics

United States

Publication History

Published: 11 July 2002

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Luong KHadi MThung FFard FLo DRastogi ATufano RBavota GArnaoudova VHaiduc S(2022)ARSeekProceedings of the 30th IEEE/ACM International Conference on Program Comprehension10.1145/3524610.3527918(331-342)Online publication date: 16-May-2022
https://dl.acm.org/doi/10.1145/3524610.3527918
Horowitz DKamvar S(2012)Searching the villageCommunications of the ACM10.1145/2133806.213383055:4(111-118)Online publication date: 1-Apr-2012
https://dl.acm.org/doi/10.1145/2133806.2133830
Horowitz DKamvar SRappa MJones PFreire JChakrabarti S(2010)The anatomy of a large-scale social search engineProceedings of the 19th international conference on World wide web10.1145/1772690.1772735(431-440)Online publication date: 26-Apr-2010
https://dl.acm.org/doi/10.1145/1772690.1772735
Navigli R(2009)Word sense disambiguationACM Computing Surveys10.1145/1459352.145935541:2(1-69)Online publication date: 23-Feb-2009
https://dl.acm.org/doi/10.1145/1459352.1459355
Purver MEhlen PNiekrasz JHovy EZechner KZhou L(2006)Shallow discourse structure for action item detectionProceedings of the HLT-NAACL 2006 Workshop on Analyzing Conversations in Text and Speech10.5555/1564535.1564540(31-34)Online publication date: 8-Jun-2006
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Purver MEhlen PNiekrasz J(2006)Detecting action items in multi-party meetingsProceedings of the Third international conference on Machine Learning for Multimodal Interaction10.1007/11965152_18(200-211)Online publication date: 1-May-2006
https://dl.acm.org/doi/10.1007/11965152_18
Le CHuynh VDam HShimazu A(2005)Combining classifiers based on OWA operators with an application to word sense disambiguationProceedings of the 10th international conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing - Volume Part I10.1007/11548669_53(512-521)Online publication date: 31-Aug-2005
https://dl.acm.org/doi/10.1007/11548669_53
Le CHuynh VShimazu A(2005)An evidential reasoning approach to weighted combination of classifiers for word sense disambiguationProceedings of the 4th international conference on Machine Learning and Data Mining in Pattern Recognition10.1007/11510888_51(516-525)Online publication date: 9-Jul-2005
https://dl.acm.org/doi/10.1007/11510888_51
Wang X(2005)Robust utilization of context in word sense disambiguationProceedings of the 5th international conference on Modeling and Using Context10.1007/11508373_40(529-541)Online publication date: 5-Jul-2005
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Wang XMatsumoto Y(2004)Trajectory based word sense disambiguationProceedings of the 20th international conference on Computational Linguistics10.3115/1220355.1220485(903-es)Online publication date: 23-Aug-2004
https://dl.acm.org/doi/10.3115/1220355.1220485
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Combining Classifiers for word sense disambiguation