Learning Probabilistic Subcategorization Preference by Identifying Case Dependencies and Optimal Noun Class Generalization Level

This paper proposes a novel method of learning probabilistic subcategorization preference. In the method, for the purpose of coping with the ambiguities of case dependencies and noun class generalization of argument/adjunct nouns, we introduce a data structure which represents a tuple of independent partial subcategorization frames. Each collocation of a verb and argument/adjunct nouns is assumed to be generated from one of the possible tuples of independent partial subcategorization frames. Parameters of subcategorization preference are then estimated so as to maximize the subcategorization preference function for each collocation of a verb and argument/adjunct nouns in the training corpus. We also describe the results of the experiments on learning probabilistic subcategorization preference from the EDR Japanese bracketed corpus, as well as those on evaluating the performance of subcategorization preference.