Chinese Document-Level Emergency Event Extraction Dataset and Corresponding Methods

Abstract

Structured extraction of emergency event information can effectively enhance the ability to respond to emergency events. This article focuses on extracting Chinese document-level emergency events, which entails addressing two key issues in this field: related datasets and the problem of role overlapping between candidate entities, which has been overlooked in existing DEE (document-level event extraction) studies that predominantly employed sequence annotation for candidate entity extraction. To tackle these challenges, we constructed a Chinese document-level emergency extraction dataset (CDEEE) and provides annotations for argument scattering, multiple events, and role overlapping. Additionally, a model named RODEE is proposed to address the role overlapping problem in DEE tasks. RODEE employs two independent modules to represent the head and tail positions of candidate entities, and utilizes a multiplication attention mechanism to interact between the two, generating a scoring matrix. Subsequently, role-overlapping candidate entities are predicted to facilitate the completion of DEE tasks. Experiments were conducted on our manually annotated dataset, CDEEE, and the results show that RODEE effectively solves the problem of role overlapping among candidate entities, resulting in improved performance of the DEE model, with an F1 value of 77.7%.

Keywords

• candidate entity extraction
• document-level event extraction
• role overlapping