General-to-Specific Model Selection for Subcategorization Preference

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 employing the maximum entropy modeling method. We also propose a new model selection algorithm which starts from the most general model and gradually examines more specific models. In the experimental evaluation, it is shown that both of the case dependencies and specific sense restriction selected by the proposed method contribute to improving the performance in subcategorization preference resolution