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Uncertainty Reduction in Collaborative Bootstrapping: Measure and Algorithm

Yunbo Cao, Hang Li, and Li Lian

Abstract

This paper proposes the use of uncertainty reduction in machine learning methods such as co-training and bilingual bootstrapping, 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.

Details

Publication typeInproceedings
URLhttp://www.aclweb.org/
PublisherAssociation for Computational Linguistics
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