Event relations specify how different event flows expressed within the context of a textual passage relate to each other in terms of temporal and causal sequences. There have already been impactful work in the area of temporal and causal event relation extraction; however, the challenge with these approaches is that (1) they are mostly supervised methods and (2) they rely on syntactic and grammatical structure patterns at the sentence-level. In this paper, we address these challenges by proposing an unsupervised event network representation for temporal and causal relation extraction that operates at the document level. More specifically, we benefit from existing Open IE systems to generate a set of triple relations that are then used to build an event network. The event network is bootstrapped by labeling the temporal disposition of events that are directly linked to each other. We then systematically traverse the event network to identify the temporal and causal relations between indirectly connected events. We perform experiments based on the widely adopted TempEval-3 and Causal-TimeBank corpora and compare our work with several strong baselines. We show that our method improves performance compared to several strong methods.

事件关系指定了在文本段落的上下文中表达的不同事件流如何在时间和因果序列上彼此相关。在时间和因果事件关系提取方面已经进行了有影响的工作；但是，这些方法所面临的挑战是：（1）它们主要是受监督的方法，（2）它们依赖句子级别的句法和语法结构模式。在本文中，我们通过提出在文档级别运行的时间和因果关系提取的无监督事件网络表示来应对这些挑战。更具体地说，我们将从现有的Open IE系统中受益，以生成一组三重关系，然后将其用于构建事件网络。通过标记彼此直接链接的事件的时间布置来引导事件网络。然后，我们系统地遍历事件网络，以识别间接连接的事件之间的时间和因果关系。我们基于广泛采用的TempEval-3和Causal-TimeBank语料库进行实验，并将我们的工作与几个可靠的基准进行比较。我们表明，与几种强大的方法相比，我们的方法可以提高性能。