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

本研究提出了一种基于深度学习的 NLP 模型，利用语义类型嵌入和动态结构编码器模块进行事件抽取和论证角色提取，用于信息抽取的关键任务之一的事件提取，并在 ACE2005 数据集上取得了显著的性能提升。

结构语义匹配实现联合事件抽取

Joint Event Extraction via Structural Semantic Matching

Event extraction involves the detection and extraction of both the event
triggers and corresponding event arguments. Existing systems often decompose
event extraction into multiple subtasks, without considering their possible
interactions. In this paper, we propose EventGraph, a joint framework for event
extraction, which encodes events as graphs. We represent event triggers and
arguments as nodes in a semantic graph. Event extraction therefore becomes a
graph parsing problem, which provides the following advantages: 1) performing
event detection and argument extraction jointly; 2) detecting and extracting
multiple events from a piece of text; and 3) capturing the complicated
interaction between event arguments and triggers. Experimental results on
ACE2005 show that our model is competitive to state-of-the-art systems and has
substantially improved the results on argument extraction. Additionally, we
create two new datasets from ACE2005 where we keep the entire text spans for
event arguments, instead of just the head word(s). Our code and models are
released as open-source.

本文介绍了 EventGraph，一种将事件编码为图形的联合框架，通过将触发事件和参数表示为语义图形中的节点，以此来解决事件提取的问题，并表明该模型在 ACE2005 上达到了和最先进的系统一样的结果，并且在参数提取方面有了显著的改善。