Jizhao Zhu, Akang Shi, Zixuan Li, Long Bai, Xiaolong Jin, Jiafeng Guo, Xueqi Cheng. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2025.

Document-level Event Extraction (DEE), a core task in natural language processing (NLP), aims to automatically identify and extract event information from documents. Despite significant progress in this area, two challenges remain: (1) existing approaches often overlook the guidance provided by event schemas during DEE; (2) they fail to simultaneously account for local and global interactions among dispersed argument mentions. To address these issues, we propose a novel DEE framework that leverages event schemas and knowledge graphs as guiding priors. Specifically, domain-specific event schemas are manually extracted and organized into a knowledge graph, serving as prior knowledge to guide the extraction process. Additionally, we construct a document knowledge graph based on positional and semantic relations among sentences and entity mentions, employing representation learning to capture complex inter-mention interactions effectively. Experiments conducted on the publicly available financial benchmark dataset ChFinAnn demonstrate that our method achieves significant improvements in F1 scores compared to state-of-the-art (SOTA) models, validating its effectiveness and advancement.