A Type Fusion and Span Relation Enhanced Event Extraction Framework for Confused Event

Fanshen Meng, Rongheng Lin

Abstract: Event extraction (EE) is a crucial aspect of information extraction facing challenges with overlapped, nested, and confused events. Most prior efforts have focused on overlapped or nested events, overlooking confused events. To address these issues, we introduce a novel event extraction model which employs type fusion to guide argument extraction tasks and utilizes span relation for event splitting to address the issue of confused event. This model simultaneously addresses the challenges of Overlapped, Nested and Confused events for Event Extraction named as ONCEE. Evaluation results on public event extraction datasets FewFC and FNDEE demonstrate a noteworthy improvement in our approach to event extraction. The average F1 score on the datasets increased by 1.8% compared to CasEE and 1.2% compared to OneEE. Besides, our model demonstrates superior performance in extracting confused events.