Maximum Entropy Model Learning of Subcategorization Preference

Abstract This paper proposes a novel method for learning probabilistic models of subcategorization preference of verbs. Especially, we propose to consider the issues of case dependencie~ and noun class generalization in a uniform way. We adopt the maximum entropy model learn~,g method and apply it to the task of model learning of subcategorization preference. Case dependencies and noun class generalization are represented as featura~ in the maximum entropy approach. The feature selection facility of the maximum entropy model learning makes it possible to find optimal case dependencies and optimal noun c ! ~ generalization levels. We describe the results of the experiment on learning probabilistic models of subcategorization preference f~om the EDR Japanese bracketed corpus. We also evaluated the performance of the selected features and their estimated parameters in the subcategorization preference task.