Research on an Event Extraction Framework Based on Two-Step Prompt Learning for Chinese Policy - Hui Ding; Huayue Gu; Pei Cao (Applied Sciences)
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.