# Chinese Document-Level Emergency Event Extraction Dataset and Corresponding Methods
## 6 Conclusions  
In this study, we constructed a Chinese document-level emergency event extraction dataset for the problem of data scarcity in Chinese document-level emergency event extraction tasks and validated it using an existing DEE model. The feasibility and research value of our constructed dataset were demonstrated. Our construction of the dataset has not only promoted the development of Chinese document-level event extraction but also laid the foundation for research on related causal knowledge graphs. Additionally, this will help relevant personnel to respond to emergencies in a timely and effective manner. In the process of constructing the dataset, we discovered the role overlapping problem of candidate entities, which is common in the real world and often overlooked in this field. To address this problem, we propose a DEE model to solve the problem and conducted experiments on our dataset. Our approach significantly improved the overall performance of the DEE task compared to the benchmark model. Specifically, we deviated from the conventional approach of using sequence labeling methods for candidate entity extraction during the candidate entity extraction phase. Instead, we utilized two modules to represent the head and tail positions of candidate entities, allowing them to interact and extract candidate entity information. This method enables the extraction of candidate entities with overlapping roles, outperforming sequence labeling methods that can only extract planar entities. Furthermore, the improved performance in the candidate entity extraction subtask further enhances the overall performance, specifically the F1 score, of the document-level event extraction task. For future work in this area, we believe that the introduction of external knowledge bases such as knowledge graphs and factual knowledge graphs will greatly improve the model's ability to cope with the role overlapping problem and thus improve the overall performance of the DEE task.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **P** & **R** & **F1** \\ \hline DE-PPN & 86.2 & 56.9 & 68.6 \\ \(-\)RoleType & 92.6 & 65.2 & 76.6 \\ +BERT & 90.5 & 63.4 & 74.5 \\
**RODEE** & **93.2** & **66.6** & **77.7** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results of ablation experiment.  
**Author Contributions:** Conceptualization, K.C. and W.Y.; methodology, K.C.; validation, K.C., F.W. and J.S.; data curation, K.C., F.W. and J.S.; writing -- original draft preparation, K.C.; writing -- review and editing, K.C., W.Y. and F.W.; supervision, K.C. and W.Y.; funding acquisition, W.Y. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the Natural Science Foundation of China under grant number 202204120017, the Autonomous Region Science and Technology Program under grant number 2022B01008-2 and 2020A02001-1.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.