Hierarchical Coordinate Structure Analysis for Japanese Statutory Sentences Using Neural Language Models

Abstract

We propose a method for analyzing the hierarchical coordinate structure of Japanese statutory sentences using neural language models (NLMs). Our method deterministically identifies hierarchical coordinate structures according to their rigorously defined descriptive rules. In addition, our method identifies all conjuncts in each coordinate structure using NLM-based scoring. Furthermore, it does not rely on any training data labeled with coordinate structures. An experiment demonstrates that our method drastically outperforms an existing method for Japanese statutory sentences.