Joint Event Extraction via Structural Semantic Matching
Haochen Li Tianhao Gao Jingkun Wang Weiping Li
Event Extraction (EE) is one of the essential tasks in information
extraction, which aims to detect event mentions from text and find the
corresponding argument roles. The EE task can be abstracted as a process of
matching the semantic definitions and argument structures of event types with
the target text. This paper encodes the semantic features of event types and
makes structural matching with target text. Specifically, Semantic Type
Embedding (STE) and Dynamic Structure Encoder (DSE) modules are proposed. Also,
the Joint Structural Semantic Matching (JSSM) model is built to jointly perform
event detection and argument extraction tasks through a bidirectional attention
layer. The experimental results on the ACE2005 dataset indicate that our model
achieves a significant performance improvement