Paper: Supervised Learning of a Probabilistic Lexicon of Verb Semantic Classes

ACL ID D09-1138
Title Supervised Learning of a Probabilistic Lexicon of Verb Semantic Classes
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2009
Authors
  • Yusuke Miyao (University of Tokyo, Tokyo Japan)
  • Jun'ichi Tsujii (University of Tokyo, Tokyo Japan; University of Manchester, Manchester UK; National Center for Text Mining, UK)

The work presented in this paper explores a supervised method for learning a prob- abilistic model of a lexicon of VerbNet classes. We intend for the probabilis- tic model to provide a probability dis- tribution of verb-class associations, over known and unknown verbs, including pol- ysemous words. In our approach, train- ing instances are obtained from an ex- isting lexicon and/or from an annotated corpus, while the features, which repre- sent syntactic frames, semantic similarity, and selectional preferences, are extracted from unannotated corpora. Our model is evaluated in type-level verb classifica- tion tasks: we measure the prediction ac- curacy of VerbNet classes for unknown verbs, and also measure the dissimilarity between the learned and observed proba- bility distributions. We em...