Paper: General-to-Specific Model Selection for Subcategorization Preference

ACL ID P98-2214
Title General-to-Specific Model Selection for Subcategorization Preference
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 1998
Authors

This paper proposes a novel method for learning probability models of subcategorization preference of verbs. We consider the issues of case dependencies and noun class generalization in a uniform way by em- ploying the maximum entropy modeling method. We also propose a new model selection algorithm which starts from the most general model and gradually ex- amines more specific models. In the experimental evaluation, it is shown that both of the case depen- dencies and specific sense restriction selected by the proposed method contribute to improving the perfor- mance in subcategorization preference resolution.