Human understanding of narrative is mainly driven by reasoning about causal
relations between events and thus recognizing them is a key capability for
computational models of language understanding. Computational work in this area
has approached this via two different routes: by focusing on acquiring a
knowledge base of common causal relations between events, or by attempting to
understand a particular story or macro-event, along with its storyline. In this
position paper, we focus on knowledge acquisition approach and claim that
newswire is a relatively poor source for learning fine-grained causal relations
between everyday events. We describe experiments using an unsupervised method
to learn causal relations between events in the narrative genres of
first-person narratives and film scene descriptions. We show that our method
learns fine-grained causal relations, judged by humans as likely to be causal
over 80% of the time. We also demonstrate that the learned event pairs do not
exist in publicly available event-pair datasets extracted from newswire.

本文主要针对通过两种方法 —— 获取常见事件因果关系的知识库和理解特定故事或宏事件，通过知识获取方法研究以认识第一人称叙述和电影场景描述等叙事类型中的事件之间因果关系。实验结果表明我们的方法可以学习细粒度的因果关系。