Event linking connects event mentions in text with relevant nodes in a knowledge base (KB). Prior research in event linking has mainly borrowed methods from entity linking, overlooking the distinct features of events. Compared to the extensively explored entity linking task, events have more complex structures and can be more effectively distinguished by examining their associated arguments. Moreover, the information-rich nature of events leads to the scarcity of event KBs. This emphasizes the need for event linking models to identify and classify event mentions not in the KB as ``out-of-KB,'' an area that has received limited attention. In this work, we tackle these challenges by introducing an argument-aware approach. First, we improve event linking models by augmenting input text with tagged event argument information, facilitating the recognition of key information about event mentions. Subsequently, to help the model handle ``out-of-KB'' scenarios, we synthesize out-of-KB training examples from in-KB instances through controlled manipulation of event arguments. Our experiment across two test datasets showed significant enhancements in both in-KB and out-of-KB scenarios, with a notable 22% improvement in out-of-KB evaluations.

翻译：事件链接将文本中的事件提及与知识库（KB）中的相关节点相连接。先前的事件链接研究主要借鉴了实体链接的方法，忽视了事件的独特特征。与广泛探索的实体链接任务相比，事件具有更复杂的结构，且通过分析其相关论元能更有效地区分事件。此外，事件富含信息的特点导致事件知识库稀缺，这使得事件链接模型需要识别并分类知识库中未提及的事件为“知识库外事件”——这一领域此前未得到充分关注。为应对这些挑战，我们提出了论元感知方法：首先，通过在输入文本中标注事件论元信息来增强事件链接模型，促进对事件提及关键信息的识别；其次，通过受控修改事件论元，从知识库内实例中合成知识库外训练样本，帮助模型处理“知识库外”场景。在两个测试数据集上的实验表明，该方法在知识库内和知识库外场景中均有显著提升，其中知识库外评估的改进幅度高达22%。