The development of event extraction systems has been hindered by the absence of wide-coverage, large-scale datasets. To make event extraction systems more accessible, we build a general-purpose event detection dataset GLEN, which covers 3,465 different event types, making it over 20x larger in ontology than any current dataset. GLEN is created by utilizing the DWD Overlay, which provides a mapping between Wikidata Qnodes and PropBank rolesets. This enables us to use the abundant existing annotation for PropBank as distant supervision. In addition, we also propose a new multi-stage event detection model specifically designed to handle the large ontology size and partial labels in GLEN. We show that our model exhibits superior performance (~10% F1 gain) compared to both conventional classification baselines and newer definition-based models. Finally, we perform error analysis and show that label noise is still the largest challenge for improving performance.

翻译：事件抽取系统的发展长期受限于缺乏广覆盖、大规模的数据集。为提升事件抽取系统的易用性，我们构建了通用事件检测数据集GLEN，覆盖3,465种不同事件类型，其本体规模较现有数据集扩大逾20倍。GLEN的构建利用了DWD覆盖层——该机制实现了维基数据节点与PropBank角色集之间的映射，使现有丰富的PropBank标注数据可作为远程监督资源。此外，我们提出了专为应对GLEN大规模本体与部分标注标签设计的多阶段事件检测模型。实验表明，相较于传统分类基线及基于定义的新兴模型，本模型展现出卓越性能（F1值提升约10%）。最后通过误差分析发现，标签噪声仍是制约性能提升的最大挑战。