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：事件提取系统的发展受到了广泛覆盖、大规模数据集的缺失的阻碍。为了使事件提取系统更易于获取，我们构建了一个通用事件检测数据集GLEN，覆盖了3465种不同的事件类型，是当前任何数据集的本体结构超过20倍。GLEN是通过利用DWD Overlay构建的，DWD Overlay提供了Wikidata Qnodes和PropBank rolesets之间的映射，使我们可以利用PropBank的现有注释作为远程监督。此外，我们还提出了一个新的多阶段事件检测模型，专门设计用于处理GLEN中大的本体结构大小和部分标签。我们展示了我们的模型相对于传统的分类基准线和新的基于定义的模型具有更高的性能（约10%的F1增益）。最后，我们进行了误差分析，表明标签噪声仍然是提高性能的最大挑战。