数字农科院2.0

Research on Agricultural Meteorological Disaster Event Extraction Method Based on Character-Word Fusion

文献类型： 外文期刊

作者： 邱明慧;;姜丽华;;谢能付;;吴焕萍;;陈颖;;李永磊

作者机构：

关键词： event extraction;agrometeorological disaster;graph convolutional network;character and word embedding fusion

期刊名称： AGRONOMY-BASEL

ISSN：

年卷期： 2025 年 15 卷 9 期

页码：

收录情况： SCIE（2025版）

摘要： Meteorological disasters are significant factors that impact agricultural production. Given the vast volume of online agrometeorological disaster information, accurately and automatically extracting essential details-such as the time, location, and extent of damage-is crucial for understanding disaster mechanisms and evolution, as well as for enhancing disaster prevention capabilities. This paper constructs a comprehensive dataset of agrometeorological disasters in China, providing a robust data foundation and strong support for event extraction tasks. Additionally, we propose a novel model named character and word embedding fusion-based GCN network (CWEF-GCN). This integration of character- and word-level information enhances the model's ability to better understand and represent text, effectively addressing the challenges of multi-events and argument overlaps in the event extraction process. The experimental results on the agrometeorological disaster dataset indicate that the F1 score of the proposed model is 81.66% for trigger classification and 63.31% for argument classification. Following the extraction of batch agricultural meteorological disaster events, this study analyzes the triggering mechanisms, damage patterns, and disaster response strategies across various disaster types using the extracted event. The findings offer actionable decision-making support for research on agricultural disaster prevention and mitigation.

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