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

翻译：事件抽取是信息抽取中的核心任务之一，旨在从文本中检测事件触发词并识别对应的论元角色。该任务可抽象为事件类型的语义定义及论元结构与目标文本之间的匹配过程。本文通过编码事件类型的语义特征，实现与目标文本的结构化匹配。具体而言，提出了语义类型嵌入和动态结构编码模块，并构建了联合结构语义匹配模型，通过双向注意力层联合执行事件检测与论元抽取任务。在ACE2005数据集上的实验结果表明，本模型取得了显著的性能提升。