Maximum Entropy Model Learning of Subcategorization Preference
Takehito Utsuro, Takashi Miyata · 1997
This paper proposes a novel method for learning probabilistic models of subcategorization preference of verbs. Especially, we propose to consider the issues of case dependencies and noun class generalization in a uniform way. We adopt the maximum entropy model learning method and apply it to the task of model learning of subcategorization preference. Case dependencies and noun class generalization are represented as features 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 class generalization levels. We describe the results of the experiment on learning probabilistic models of subcategorization preference from the EDR Japanese bracketed corpus. We also evaluated the performance of the selected features and their estimated parameters in the subcategorization preference task. 1 Introduction In corpus-based NLP, extraction of linguistic knowledge such as lexical/sem...