Paper: A Semi-Supervised Feature Clustering Algorithm With Application To Word Sense Disambiguation

ACL ID H05-1114
Title A Semi-Supervised Feature Clustering Algorithm With Application To Word Sense Disambiguation
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2005
Authors

In this paper we investigate an applica- tion of feature clustering for word sense disambiguation, and propose a semi- supervised feature clustering algorithm. Compared with other feature clustering methods (ex. supervised feature cluster- ing), it can infer the distribution of class labels over (unseen) features unavailable in training data (labeled data) by the use of the distribution of class labels over (seen) features available in training data. Thus, it can deal with both seen and unseen fea- tures in feature clustering process. Our ex- perimental results show that feature clus- tering can aggressively reduce the dimen- sionality of feature space, while still main- taining state of the art sense disambigua- tion accuracy. Furthermore, when com- bined with a semi-supervised WSD algo- ...