Event coreference continues to be a challenging problem in information extraction. With the absence of any external knowledge bases for events, coreference becomes a clustering task that relies on effective representations of the context in which event mentions appear. Recent advances in contextualized language representations have proven successful in many tasks, however, their use in event linking been limited. Here we present a three part approach that (1) uses representations derived from a pretrained BERT model to (2) train a neural classifier to (3) drive a simple clustering algorithm to create coreference chains. We achieve state of the art results with this model on two standard datasets for within-document event coreference task and establish a new standard on a third newer dataset.

翻译：事件共指消解仍然是信息抽取领域的一个具有挑战性的问题。由于缺乏事件的外部知识库，共指消解成为一项依赖事件提及上下文有效表示的聚类任务。近年来，上下文语言表示的最新进展已在许多任务中取得成效，但其在事件链接中的应用仍十分有限。本文提出一种三部分方法：(1) 利用预训练BERT模型生成的表示， (2) 训练神经分类器， (3) 驱动简单聚类算法以构建共指链。该方法在两个标准文档内事件共指消解数据集上取得了最先进的结果，并在第三个较新的数据集上树立了新标杆。