Text Classification from Labeled and Unlabeled Documents using EM
Machine Learning - Special issue on information retrieval
Employing EM and Pool-Based Active Learning for Text Classification
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
An empirical study of the behavior of active learning for word sense disambiguation
HLT-NAACL '06 Proceedings of the main conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics
Hi-index | 0.00 |
This paper proposes to solve the bottleneck of finding training data for word sense disambiguation (WSD) in the domain of web queries, where a complete set of ambiguous word senses are unknown. In this paper, we present a combination of active learning and semi-supervised learning method to treat the case when positive examples, which have an expected word sense in web search result, are only given. The novelty of our approach is to use "pseudo negative examples" with reliable confidence score estimated by a classifier trained with positive and unlabeled examples. We show experimentally that our proposed method achieves close enough WSD accuracy to the method with the manually prepared negative examples in several Japanese Web search data.