Towards intelligent policy analysis: A discourse structure parsing technique for Chinese government document

Abstract

Government document analysis can help organizations to understand key policy points and make right response. To realize intelligent analysis of a volume of policies, we propose a discourse parsing technique for in-depth understanding of Chinese government documents (CGDs). Based on Superstructure Schema and Rhetorical Structure Theory (RST), we characterize the stylistic features and macrostructure patterns of CGDs and develop a discourse analysis framework to specify its functional structure and semantic system. A tree schema is adopted to portray the hierarchy of textual fragments in full text and formalize their representations in machine-based parsing, then a multi-head partition self-attention model is constructed to evaluate the discourse analysis framework. Experiment results show that our parsing model embedding with inherent CGD discourse features performs better than baselines. The highest f1-score in span and label prediction tasks researches 0.9209. The parsing model can be migrated to other discourse parsing tasks by considering specific genre features of different documents.