Paper: Partially Supervised Sense Disambiguation By Learning Sense Number From Tagged And Untagged Corpora

ACL ID W06-1649
Title Partially Supervised Sense Disambiguation By Learning Sense Number From Tagged And Untagged Corpora
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2006
Authors

Supervised and semi-supervised sense dis- ambiguation methods will mis-tag the in- stances of a target word if the senses of these instances are not de ned in sense in- ventories or there are no tagged instances for these senses in training data. Here we used a model order identi cation method to avoid the misclassi cation of the in- stances with unde ned senses by discov- ering new senses from mixed data (tagged and untagged corpora). This algorithm tries to obtain a natural partition of the mixed data by maximizing a stability cri- terion de ned on the classi cation result from an extended label propagation al- gorithm over all the possible values of the number of senses (or sense number, model order). Experimental results on SENSEVAL-3 data indicate that it outper- forms SVM, a one-clas...