@InProceedings{hu-rahimtoroghi-walker:2017:EventStory,
  author    = {Hu, Zhichao  and  Rahimtoroghi, Elahe  and  Walker, Marilyn},
  title     = {Inference of Fine-Grained Event Causality from Blogs and Films},
  booktitle = {Proceedings of the Events and Stories in the News Workshop},
  month     = {August},
  year      = {2017},
  address   = {Vancouver, Canada},
  publisher = {Association for Computational Linguistics},
  pages     = {52--58},
  abstract  = {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.},
  url       = {http://www.aclweb.org/anthology/W17-2708}
}