Paper: Uncertainty Reduction In Collaborative Bootstrapping: Measure And Algorithm

ACL ID P03-1042
Title Uncertainty Reduction In Collaborative Bootstrapping: Measure And Algorithm
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2003
Authors

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 gen- eral term, as ‘collaborative bootstrapping’. The paper indicates that uncertainty re- duction is an important factor for enhanc- ing the performance of collaborative bootstrapping. It proposes a new measure for representing the degree of uncertainty correlation of the two classifiers in col- laborative bootstrapping and uses the measure in analysis of collaborative boot- strapping. Furthermore, it proposes a new algorithm of collaborative bootstrapping on the basis of uncertainty reduction. Ex- perimental results have verified the cor- rectness of the analysis and have demonstrated the significance of the new algorithm.