Event detection plays an essential role in the task of event extraction. It aims at identifying event trigger words in a sentence and classifying event types. Generally, multiple event types are usually well-organized with a hierarchical structure in real-world scenarios, and hierarchical correlations between event types can be used to enhance event detection performance. However, such kind of hierarchical information has received insufficient attention which can lead to misclassification between multiple event types. In addition, the most existing methods perform event detection in Euclidean space, which cannot adequately represent hierarchical relationships. To address these issues, we propose a novel event detection network HyperED which embeds the event context and types in Poincaré ball of hyperbolic geometry to help learn hierarchical features between events. Specifically, for the event detection context, we first leverage the pre-trained BERT or BiLSTM in Euclidean space to learn the semantic features of ED sentences. Meanwhile, to make full use of the dependency knowledge, a GNN-based model is applied when encoding event types to learn the correlations between events. Then we use a simple neural-based transformation to project the embeddings into the Poincaré ball to capture hierarchical features, and a distance score in hyperbolic space is computed for prediction. The experiments on MAVEN and ACE 2005 datasets indicate the effectiveness of the HyperED model and prove the natural advantages of hyperbolic spaces in expressing hierarchies in an intuitive way.

中文翻译：

---

HyperED：基于双曲几何的层次感知网络，用于事件检测

事件检测在事件提取任务中起着至关重要的作用。它的目的是识别句子中的事件触发词并对事件类型进行分类。一般来说，在现实场景中，多种事件类型通常以层次结构组织良好，事件类型之间的层次相关性可用于增强事件检测性能。然而，这种分层信息没有受到足够的重视，这可能导致多种事件类型之间的错误分类。此外，大多数现有方法在欧几里得空间中执行事件检测，这不能充分表示层次关系。为了解决这些问题，我们提出了一种新颖的事件检测网络 HyperED，它将事件上下文和类型嵌入双曲几何的庞加莱球中，以帮助学习事件之间的层次特征。具体来说，对于事件检测上下文，我们首先利用欧几里德空间中预训练的 BERT 或 BiLSTM 来学习 ED 句子的语义特征。同时，为了充分利用依赖知识，在编码事件类型时应用基于 GNN 的模型来学习事件之间的相关性。然后，我们使用简单的基于神经的变换将嵌入投影到庞加莱球中以捕获分层特征，并计算双曲空间中的距离分数以进行预测。在MAVEN和ACE 2005数据集上的实验表明了HyperED模型的有效性，并证明了双曲空间在直观表达层次结构方面的天然优势。