  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.
