Neural Network-Based Model for Japanese Predicate Argument Structure Analysis

Abstract

This paper presents a novel model for Japanese predicate argument structure (PAS) analysis based on a neural network framework. Japanese PAS analysis is challenging due to the tangled characteristics of the Japanese language, such as case disappearance and argument omission. To unravel this problem, we learn selectional preferences from a large raw corpus, and incorporate them into a SOTA PAS analysis model, which considers the consistency of all PASs in a given sentence. We demonstrate that the proposed PAS analysis model significantly outperforms the base SOTA system.