Event Argument Extraction for Rainstorm Disasters Based on Social Media: A Case Study of the 2021 Heavy Rains in Henan

Abstract

Rainstorm disasters have wide-ranging impacts on communities, but traditional information collection methods are often hampered by high labor costs and limited coverage. Social media platforms such as Weibo provide new opportunities for monitoring and analyzing disaster-related information in real-time. In this paper, we present ETEN_BERT_QA, a novel model for extracting event arguments from Weibo rainstorm disaster texts. The model incorporates the event text enhancement network (ETEN) to enhance the extraction process by improving the semantic representation of event information in combination with event trigger words. To support our approach, we constructed RainEE, a dataset dedicated to rainstorm disaster event extraction, and implemented a two-step process, as follows: (1) event detection, which identifies trigger words and classifies them into event types, and (2) event argument extraction, which identifies event arguments and classifies them into argument roles. Our ETEN_BERT_QA model combines ETEN with a BERT-based question-answering mechanism to further improve the understanding of the event text. Experimental evaluations on the RainEE and DuEE datasets show that ETEN_BERT_QA significantly outperforms the baseline model in terms of accuracy and the number of event argument extractions, validating its effectiveness in analyzing rainstorm disaster-related Weibo texts.