利用文档级信息结合语义空间加强事件检测

事件检测(ED)是事件抽取的一项基础任务,旨在检测事件触发器并进行分类.现有事件检测方法主要基于句子级信息,忽略了句子间的事件相关性.文档级信息有助于减轻语义歧义与加强上下文理解,为此,提出一种新颖的事件检测框架,命名为document embedding networks combined with semantic...

Exploiting Document-Level Information to Enhance Event Detection Combined with Semantic Space

Event detection (ED) is a fundamental task of event extraction, which aims to detect triggers in text and determine their event types. Most existing methods regard event detection as a sentence-level classification problem, ignoring the correlations between events in different sentences. A novel event detection framework, named document embedding networks combined with semantic space (DENSS), is proposed in this paper. The document-level information is utilized to alleviate semantic ambiguity and enhance contextual understanding. Specifically, the representations of event types and triggers are obtained through an off-the-shelf pre-trained model and a designed multi-level attention mechanism. Then the feature vectors of event types and triggers are mapped into a shared semantic space, where the distance represents the correlation of different events. The experimental results on the benchmark dataset demonstrate that our method outperforms most existing methods, and justify the effectiveness of document-level information with shared semantic space.