# Research on an Event Extraction Framework Based on Two-Step Prompt Learning for Chinese Policy
###### Abstract  
The Chinese government releases a large number of enterprise-friendly policy documents every year, but due to the dispersed policy release channels and complex document parsing, enterprises need to invest a lot of human and material resources to analyze them, which results in many enterprises missing the opportunity to file. To solve this problem, this paper proposes a two-step cue learning-based event extraction framework (TPEE), which learns to recognize the role start and stop markers in the text by two prompts, and introduces a dynamic weighting mechanism to enhance the information interaction between roles. In the experiments, TPEE shows strong generalization ability on the publicly available financial dataset DuEE-Fin, and improves the F1-scores in argument recognition and classification tasks by 0.84% and 0.74%, respectively. In addition, based on the policy dataset DEE-Policy constructed by the authors, TPEE achieves F1-scores of 73.92% and 72.81% in the argument recognition and classification tasks, respectively, which are improved by 2.14% and 2.26% compared to the baseline model. The results show that the TPEE framework performs well in event extraction in the policy domain and provides technical support for enterprises to obtain policy information efficiently.  
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Footnote †: journal: Applied sciences  
16 January 202514 March  
performance in practical applications due to their negligence of interdependent semantic relationships within policy documents. Through systematic comparative analysis, we find that enterprise declaration requirements in policy texts are mainly presented in the form of events, e.g., policies about fund declaration usually require conditions such as unit type and creation time. For this key feature, we propose an innovative event extraction technique for accurately identifying policy filing requirements. Compared with the above method of extracting declaration conditions using keyword technology, it can better adapt to the structure of policy text and accurately extract the corresponding declaration conditions.  
There are some challenges in applying event extraction to the policy domain: (1) As far as we know, this is the first attempt to apply event extraction technologies to the Chinese policy field. Therefore, there are no standard experimental data. (2) Policy text is mostly in the form of documents, while the text contains a large number of long and continuous arguments. As shown in Figure 1: in the event \" (financial support)\", the \" (unit type)\" has a number of consecutive arguments in the text: \" (private enterprises)\", \" (joint-stock companies)\", \" (cooperatives)\" and \" ( foreign-funded enterprises)\". In addition, the \" (services)\": \" (services)\": \" (services)\", \" (  
Therefore, this paper explores prompt-based learning for extraction and introduces the Two-step Prompt learning Event Extraction Framework (TPEE). It integrates all argument roles into the prompt, allowing role information to interact with PLMs and enabling joint selection of optimal argument spans via specific role queries. To leverage semantic associations between roles, a dynamic weighting mechanism is introduced to refine role representations based on the prompt. It is designed in this paper to solve the problem of long arguments extraction. The main contribution of this paper consists of three parts: (1) In this paper, the TPEE model is innovatively proposed, which can efficiently extract policy events by designing two-step prompts; (2) Capturing parameter relationships using a dynamic weighting mechanism; (3) In this paper, the method is validated on DEE-Policy and DuEE-Fin to demonstrate the effectiveness of TPEE.  
The structure of the paper is as follows: Section 2 reviews related work, Section 3 provides a detailed description of the model, Section 4 presents our experimental study, and Section 5 offers a summary of the paper along with discussion of future research directions.