摘要：

Abstract:

【Purpose/significance】 In the field of conflict events, there is a lack of datasets, and existing event extraction methods heav⁃

ily rely on large amounts of data, leading to poor training effectiveness and insufficient generalization ability in low-resource scenarios.

To address these issues, this paper proposes a few-shot event extraction method, PriorPromptEE, based on prior prompt templates.

【Method/process】 Firstly, this paper improves the conflict event representation framework based on the political and military dimen⁃

sion, and constructs a few-shot conflict event extraction dataset using a semi-automatic labeling method. Secondly, a pipeline is used

to design and construct strategies for prompt template generation. The prompt templates are embedded in the model's encoding layer

based on prior knowledge, and the output layer employs a double-pointer mechanism to extract entity start and end positions, thus

achieving event argument prediction.【Result/conclusion】 Experimental results show that the proposed event extraction method, which

incorporates prior prompt templates, performs well on few-shot conflict event dataset. Compared to sequence models, the score is im⁃

proved by 42% to 45%, compared to traditional event extraction models, the score is improved by 19% to 45%, and compared to zero

shot event extraction models, the score is improved by 19%. The score of the proposed model, PriorPromptEE, reaches up to 0.85,

validating the effectiveness of this approach.【Innovation/limitation】This paper improves the framework for representing conflict

events from the perspective of international relations of political and military, and provides a new approach for identifying conflict

types and extracting core elements based on prior prompt templates. In the future, the framework for representing conflict events could

be further enhanced from economic, public opinion, and other dimensions to strengthen the model framework for conflict event extrac⁃

tion.