Causality Extraction in Chinese Public Health Events Text

Extracting causality in public health event datasets is crucial, and traditional sentence-level extraction methods have been extensively studied. However, the performance of widely used models remains poor, especially for Chinese datasets. One reason is the lack of high-quality labeled Chinese datasets in this field. Additionally, implicit causality, cross-sentence causality, and multiple causalities in Chinese datasets make it difficult for models to fully extract causality. To address these issues, we constructed the first Chinese public health event dataset for causality extraction, containing 33,286 Weibo texts. We propose a model with multi-task learning to provide additional information and an attention mechanism to focus on key context for causality. The model achieved an F1 score of 0.9554 on our dataset and performed well in multiple causalities and cross-sentence causality. Our work focuses on short-text relationship extraction in the context of public health events, addressing the unique challenges of implicit causality and cross-sentence dependencies.