Document-level event argument extraction (DEAE) aims to identify the arguments corresponding to the roles of a given event type in a document. However, arguments scattering and arguments and roles overlapping make DEAE face great challenges. In this paper, we propose a novel DEAE model called Role Knowledge Prompting for Document-Level Event Argument Extraction (RKDE), which enhances the interaction between templates and roles through a role knowledge guidance mechanism to precisely prompt pretrained language models (PLMs) for argument extraction. Specifically, it not only facilitates PLMs to understand deep semantics but also generates all the arguments simultaneously. The experimental results show that our model achieved decent performance on two public DEAE datasets, with 3.2% and 1.4% F1 improvement on Arg-C, and to some extent, it addressed the overlapping arguments and roles.

Event causality identification (ECI) aims to identify causality between event pairs in a text, and is commonly approached as a supervised classification ta

With the multi-source intelligence data grow exponentially, traditional methods can barely satisfy the requirement, which means to organize and understand the tremendous military knowledge. So, this research project aims to develop unified, highly connected military knowledge graph to integrate information. Using front-end interaction technology, supporting learning function based on inference, error-correcting and marking, this graph can continuously modify the logic model and improve the intelligence of itself, so the knowledge will be solid in our department. It can reduce dependence on the experience of special experts, and moreover, provide platform for developing data-driven general battle system.