# Research on an Event Extraction Framework Based on Two-Step Prompt Learning for Chinese Policy
## 5 Conclusions  
In this paper, an event extraction model TPEE based on a two-step prompt is proposed. The model designs the second step prompt based on the LLMs and the initial prompt to precisely extract arguments from the input text. In addition, a dynamic weighting method is also used to enhance the interaction between the first extraction results and the elements of the precise prompt, and finally, improve the accuracy of the policy field event extraction. Other than that. a complete policy domain dataset DEE-Policy is constructed for future event extraction efforts in the policy domain.  
Experimental results on the Chinese policy domain dataset show that the TPEE framework proposed in this paper is superior to the baseline model. It is worth mentioning that TPEE also achieves good results on publicly available datasets in the financial field with similar text structures, which indicates that the method in this paper has strong generalization.  
In addition, through case analysis, it can be found that in the identification process, some arguments that are far from other argument elements are often not easy to identify. In the future, we plan to introduce domain vocabulary information and paragraph abstract semantic information to overcome the difficulty of domain vocabulary recognition and fragmentation of argument elements.  
Conceptualization, H.D. and H.G.; methodology, H.D. and H.G.; software, H.D. and H.G.; validation, H.G. and P.C.; formal analysis, H.G.; investigation, H.G.; resources, H.G. and H.D.; data curation, H.G. and H.D.; writing--original draft preparation, H.G.; writing--review and editing, H.G.; visualization, H.G.; supervision, P.C.; project administration, H.D.; funding acquisition, H.D. All authors have read and agreed to the published version of the manuscript.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Batch/Arg Classification** & **P** & **R** & **F1** \\ \hline
1 & 79.31 & 63.32 & 70.41 \\
2 & 78.50 & 64.16 & 70.61 \\
3 & 77.48 & 61.42 & 68.52 \\
4 & 76.53 & 69.43 & 72.81 \\
5 & 76.70 & 64.09 & 69.83 \\
**lr/Arg Classification** & **P** & **R** & **F1** \\ \(1\times 10^{-5}\) & 75.99 & 67.18 & 71.32 \\ \(2\times 10^{-5}\) & 74.22 & 63.32 & 68.34 \\ \(3\times 10^{-5}\) & 78.85 & 64.72 & 71.09 \\ \(4\times 10^{-5}\) & 77.27 & 64.02 & 70.02 \\ \(5\times 10^{-5}\) & 76.53 & 69.43 & 72.81 \\
**steps/Arg Classification** & **P** & **R** & **F1** \\
5000 & 80.40 & 59.10 & 68.12 \\
8000 & 76.97 & 63.86 & 69.82 \\
10,000 & 76.53 & 69.43 & 72.81 \\
15,000 & 77.68 & 68.23 & 72.65 \\
20,000 & 79.39 & 66.06 & 72.11 \\ \hline \hline \end{tabular}
\end{table}
Table 11: Results of parameter sensitivity experiments.  
**Funding:** This work was supported by the 2021 Hefei College Talent Research Fund Project: 21-22 RC13 and the Natural Science Foundation of the AnHui Higher Education Institutions of China: Grant 2022 AH051779.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The author confirms that some of the policy data covered in the article are available at [[https://github.com/ghy0816/DEE-Policy](https://github.com/ghy0816/DEE-Policy)]([https://github.com/ghy0816/DEE-Policy](https://github.com/ghy0816/DEE-Policy)) (8 March 2025), and the full data can be obtained by contacting the author ([PERSON]).  
**Conflicts of Interest:** The authors declare no conflicts of interest.