General-to-Speci c 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 speci c models. In the experimental evaluation, it is shown that both of the case dependencies and speci c sense restriction selected by the proposed method contribute to improving the performance in subcategorization preference resolution.