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一直受到缺乏广覆盖、大规模数据集的阻碍。为使事件抽取系统更易获取，我们构建了一个通用事件检测数据集GLEN，涵盖3,465种不同事件类型，其本体规模是现有数据集的20倍以上。GLEN通过利用DWD Overlay创建，该工具提供了维基数据节点与PropBank角色集之间的映射关系，从而使我们能够将现有丰富的PropBank标注作为远程监督。此外，我们还提出了一种专为处理GLEN中大规模本体和部分标注而设计的多阶段事件检测模型。实验表明，与传统的分类基线和基于定义的新模型相比，我们的模型性能显著提升（F1值提高约10%）。最后，我们通过误差分析指出，标签噪声仍是提升性能的最大挑战。