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Uncertainty reduction in collaborative bootstrapping: measure and algorithm

Authors:

Yunbo Cao,

Hang Li,

Li LianAuthors Info & Claims

ACL '03: Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1

Pages 327 - 334

https://doi.org/10.3115/1075096.1075138

Published: 07 July 2003 Publication History

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Abstract

This paper proposes the use of uncertainty reduction in machine learning methods such as co-training and bilingual boot-strapping, which are referred to, in a general term, as 'collaborative bootstrapping'. The paper indicates that uncertainty reduction is an important factor for enhancing the performance of collaborative bootstrapping. It proposes a new measure for representing the degree of uncertainty correlation of the two classifiers in collaborative bootstrapping and uses the measure in analysis of collaborative bootstrapping. Furthermore, it proposes a new algorithm of collaborative bootstrapping on the basis of uncertainty reduction. Experimental results have verified the correctness of the analysis and have demonstrated the significance of the new algorithm.

References

[1]

S. Abney, 2002. Bootstrapping. In Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics.

Digital Library

Google Scholar

[2]

A. Blum and T. Mitchell, 1998. Combining Labeled Data and Unlabelled Data with Co-training. In Proceedings of the 11th Annual Conference on Computational learning Theory.

Digital Library

Google Scholar

[3]

M. Collins and Y. Singer, 1999. Unsupervised Models for Named Entity Classification. In Proceedings of the 1999 Joint SIGDAT Conference on Empirical Methods in Natural Language Processing and Very Large Corpora.

Google Scholar

[4]

S. Dasgupta, M. Littman and D. McAllester, 2001. PAC Generalization Bounds for Co-Training. In Proceedings of Neural Information Processing System, 2001.

Google Scholar

[5]

T. Joachims, 1997. A Probabilistic Analysis of the Rocchio Algorithm with TFIDF for Text Categorization. In Proceedings of the 14th International Conference on Machine Learning.

Digital Library

Google Scholar

[6]

D. Lewis and W. Gale, 1994. A Sequential Algorithm for Training Text Classifiers. In Proceedings of the 17th International ACM-SIGIR Conference on Research and Development in Information Retrieval.

Digital Library

Google Scholar

[7]

C. Li and H. Li, 2002. Word Translation Disambiguation Using Bilingual Bootstrapping. In Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics.

Digital Library

Google Scholar

[8]

I. Muslea, S. Minton, and C. A. Knoblock 2000. Selective Sampling With Redundant Views. In Proceedings of the Seventeenth National Conference on Artificial Intelligence.

Digital Library

Google Scholar

[9]

K. Nigam and R. Ghani, 2000. Analyzing the Effectiveness and Applicability of Co-Training. In Proceedings of the 9th International Conference on Information and Knowledge Management.

Digital Library

Google Scholar

[10]

D. Pierce and C. Cardie 2001. Limitations of Co-Training for Natural Language Learning from Large Datasets. In Proceedings of the 2001 Conference on Empirical Methods in Natural Language Processing (EMNLP-2001).

Google Scholar

[11]

D. Yarowsky, 1995. Unsupervised Word Sense Disambiguation Rivaling Supervised Methods. In Proceedings of the 33rd Annual Meeting of the Association for Computational Linguistics.

Digital Library

Google Scholar

Cited By

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Oh JTorisawa KHashimoto CIida RTanaka MKloetzer J(2016)A semi-supervised learning approach to why-question answeringProceedings of the Thirtieth AAAI Conference on Artificial Intelligence10.5555/3016100.3016325(3022-3029)Online publication date: 12-Feb-2016
https://dl.acm.org/doi/10.5555/3016100.3016325
Ji HJoshi AHuang CJurafsky D(2010)Challenges from information extraction to information fusionProceedings of the 23rd International Conference on Computational Linguistics: Posters10.5555/1944566.1944624(507-515)Online publication date: 23-Aug-2010
https://dl.acm.org/doi/10.5555/1944566.1944624
Shen DZhang JSu JZhou GTan C(2004)A collaborative ability measurement for co-trainingProceedings of the First international joint conference on Natural Language Processing10.1007/978-3-540-30211-7_46(436-445)Online publication date: 22-Mar-2004
https://dl.acm.org/doi/10.1007/978-3-540-30211-7_46
Show More Cited By

Uncertainty reduction in collaborative bootstrapping: measure and algorithm
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning
2. Hardware
  1. Power and energy
    1. Power estimation and optimization

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ACL '03: Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1

July 2003

571 pages

Program Chairs:
Erhard W. Hinrichs,
Dan Roth

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Association for Computational Linguistics

United States

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Published: 07 July 2003

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Oh JTorisawa KHashimoto CIida RTanaka MKloetzer J(2016)A semi-supervised learning approach to why-question answeringProceedings of the Thirtieth AAAI Conference on Artificial Intelligence10.5555/3016100.3016325(3022-3029)Online publication date: 12-Feb-2016
https://dl.acm.org/doi/10.5555/3016100.3016325
Ji HJoshi AHuang CJurafsky D(2010)Challenges from information extraction to information fusionProceedings of the 23rd International Conference on Computational Linguistics: Posters10.5555/1944566.1944624(507-515)Online publication date: 23-Aug-2010
https://dl.acm.org/doi/10.5555/1944566.1944624
Shen DZhang JSu JZhou GTan C(2004)A collaborative ability measurement for co-trainingProceedings of the First international joint conference on Natural Language Processing10.1007/978-3-540-30211-7_46(436-445)Online publication date: 22-Mar-2004
https://dl.acm.org/doi/10.1007/978-3-540-30211-7_46
Feng HChua TSebe NLew MDjeraba C(2003)A bootstrapping approach to annotating large image collectionProceedings of the 5th ACM SIGMM international workshop on Multimedia information retrieval10.1145/973264.973274(55-62)Online publication date: 7-Nov-2003
https://dl.acm.org/doi/10.1145/973264.973274

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Input Uncertainty Quantification via Simulation Bootstrapping

Monotonic uncertainty measures for attribute reduction in probabilistic rough set model

Numerical approach for quantification of epistemic uncertainty

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