A fully-lexicalized probabilistic model for Japanese syntactic and case structure analysis

Abstract

We present an integrated probabilistic model for Japanese syntactic and case structure analysis. Syntactic and case structure are simultaneously analyzed based on wide-coverage case frames that are constructed from a huge raw corpus in an unsupervised manner. This model selects the syntactic and case structure that has the highest generative probability. We evaluate both syntactic structure and case structure. In particular, the experimental results for syntactic analysis on web sentences show that the proposed model significantly outperforms known syntactic analyzers.