# Generating Chinese Event Extraction Method Based on ChatGPT and Prompt Learning
## 5 Conclusions  
Given the scarcity of annotated data for event extraction tasks and the need for more semantic exploration of event extraction models, this paper proposes a generative event extraction model CPEE based on ChatGPT and prompt learning with event extraction as the research object. The model extends and improves existing methods in two aspects: data augmentation and input construction. Firstly, to address the annotated data scarcity, this paper uses ChatGPT to generate a labelled dataset for event extraction tasks and trains the model using supervised learning methods adapted to downstream tasks. Secondly, to fully explore the rich semantic knowledge within the model, the paper proposes the construction of generative input templates and explicitly adds entity markers within the templates. The experimental results on the public datasets DuEE1.0 and Title2 Event demonstrate that the proposed model achieves higher extraction accuracy than existing event extraction methods based on reading comprehension and sequence labelling. It provides a valuable solution for event extraction tasks in the Chinese domain. The application of ChatGPT in generating labelled data for event extraction tasks can alleviate the high costs of annotating data for new domains and events, thus possessing significant practical value. The ablation experiment further confirms that the ChatGPT-annotated data corpus is more helpful in improving the model's performance.  
When using ChatGPT for data enhancement, the recall phase for ChatGPT in the early stage is very critical, and it is necessary to guide and correct the content of ChatGPT's answers, so that it fully understands the inputs and outputs of the task of generating an event extraction corpus; in the process of generating, it is necessary to adjust the consistency of the trigger word, event arguments, and the generated text in a timely manner, and the effect of the trained model is not ideal if it is only pursuing the rapid generation of a large number of event extraction labeled corpora without controlling the quality of labeled precursors. However, the improvements in this paper are limited to data augmentation and input construction. In the future, it would be worth considering optimizing the model's internal structure through prompt tuning to enhance the model's understanding and generation capabilities for event semantics. Additionally, the use of ChatGPT for generating annotated data is currently explored only for event extraction tasks. In the future, researchers could explore their application to other natural language processing tasks, reducing reliance on large-scale annotated data and expanding its applicability, thereby promoting the development and advancement of related tasks.  
Conceptualization, J.C. and P.C.; Software, X.W.; Formal analysis, P.C.; Data curation, J.C.; Writing--original draft, J.C.; Visualization, X.W.; Funding acquisition, P.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by 2022 Post Graduate Course Construction Project of People's Public Security University of China (2022 YJSKCJS037) and Fundamental Research Funds for the Central Universities (2022)KF02018).  
Not applicable.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & **0** & **4000** & **8000** & **12,000** & **16,000** \\ \hline \multirow{4}{*}{Title2 Event} & CPEE & 57.7 & 59.9 & 61.2 & 62.0 & 62.3 \\  & BERT-CRF & 41.2 & 44.4 & 45.3 & 46.0 & 46.1 \\ \cline{1-1}  & MRC & 44.7 & 47.9 & 49.9 & 50.9 & 51.4 \\ \cline{1-1}  & Seq2 seqMRC & 49.9 & 51.9 & 53.5 & 53.8 & 54.4 \\ \hline \hline \end{tabular}
\end{table}
Table 6: ChatGPT-annotated corpus experimental results.