DocEE-zh: A Fine-grained Benchmark for Chinese Document-level Event Extraction
Minghui Liu, Meihan Tong, Yangda Peng, Lei Hou, Juanzi Li, Bin Xu · 2024
Event extraction aims to identify events and then extract the arguments involved in those events.In recent years, there has been a gradual shift from sentence-level event extraction to document-level event extraction research.Despite the significant success achieved in English domain event extraction research, event extraction in Chinese still remains largely unexplored.However, a major obstacle to promoting Chinese document-level event extraction is the lack of fine-grained, wide domain coverage datasets for model training and evaluation.In this paper, we propose DocEE-zh, a new Chinese document-level event extraction dataset comprising over 36,000 events and more than 210,000 arguments.DocEE-zh is an extension of the DocEE dataset, utilizing the same event schema, and all data has been meticulously annotated by human experts.We highlight two features: focus on high-interest event types and fine-grained argument types.Experimental results indicate that state-of-theart models still fail to achieve satisfactory performance, with an F1 score of 45.88% on the event argument extraction task, revealing that Chinese document-level event extraction (Do-cEE) remains an unresolved challenge.DocEEzh is now available at https://github.com/ tongmeihan1995/DocEE.git.