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Published: 06 July 2002 Publication History

Abstract

This paper refines the analysis of co-training, defines and evaluates a new co-training algorithm that has theoretical justification, gives a theoretical justification for the Yarowsky algorithm, and shows that co-training and the Yarowsky algorithm are based on different independence assumptions.

References

[1]
A. Blum and T. Mitchell. 1998. Combining labeled and unlabeled data with co-training. In COLT: Proceedings of the Workshop on Computational Learning Theory. Morgan Kaufmann Publishers.
[2]
Michael Collins and Yoram Singer. 1999. Unsupervised models for named entity classification. In EMNLP.
[3]
Sanjoy Dasgupta, Michael Littman, and David McAllester. 2001. PAC generalization bounds for co-training. In Proceedings of NIPS.
[4]
David Yarowsky. 1994. Decision lists for lexical ambiguity resolution. In Proceedings ACL 32.
[5]
David Yarowsky. 1995. Unsupervised word sense disambiguation rivaling supervised methods. In Proceedings of the 33rd Annual Meeting of the Association for Computational Linguistics, pages 189--196.

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Information & Contributors

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cover image DL Hosted proceedings
ACL '02: Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
July 2002
543 pages

Publisher

Association for Computational Linguistics

United States

Publication History

Published: 06 July 2002

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Overall Acceptance Rate 85 of 443 submissions, 19%

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Cited By

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  • (2024)A deep learning approach for the comparison of handwritten documents using latent feature vectorsStatistical Analysis and Data Mining10.1002/sam.1166017:1Online publication date: 9-Feb-2024
  • (2023)TGEditor: Task-Guided Graph Editing for Augmenting Temporal Financial Transaction NetworksProceedings of the Fourth ACM International Conference on AI in Finance10.1145/3604237.3626883(219-226)Online publication date: 27-Nov-2023
  • (2021)Self-Supervised Graph Co-Training for Session-based RecommendationProceedings of the 30th ACM International Conference on Information & Knowledge Management10.1145/3459637.3482388(2180-2190)Online publication date: 26-Oct-2021
  • (2019)Towards making co-training suffer less from insufficient viewsFrontiers of Computer Science: Selected Publications from Chinese Universities10.1007/s11704-018-7138-513:1(99-105)Online publication date: 1-Feb-2019
  • (2019)Adaptive Feature Selection and Feature Fusion for Semi-supervised ClassificationJournal of Signal Processing Systems10.1007/s11265-018-1355-x91:5(521-537)Online publication date: 1-May-2019
  • (2017)Self-paced co-trainingProceedings of the 34th International Conference on Machine Learning - Volume 7010.5555/3305890.3305916(2275-2284)Online publication date: 6-Aug-2017
  • (2016)Support Vector Machine with Ensemble Tree Kernel for Relation ExtractionComputational Intelligence and Neuroscience10.1155/2016/84957542016(15)Online publication date: 1-Mar-2016
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  • (2015)MUVIRProceedings of the 24th International Conference on Artificial Intelligence10.5555/2832747.2832820(4098-4104)Online publication date: 25-Jul-2015
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