@InProceedings{ahn:2017:EventStory,
  author    = {Ahn, Natalie},
  title     = {Inducing Event Types and Roles in Reverse: Using Function to Discover Theme},
  booktitle = {Proceedings of the Events and Stories in the News Workshop},
  month     = {August},
  year      = {2017},
  address   = {Vancouver, Canada},
  publisher = {Association for Computational Linguistics},
  pages     = {66--76},
  abstract  = {With growing interest in automated event extraction, there is an increasing
	need to overcome the labor costs of hand-written event templates, entity lists,
	and annotated corpora. In the last few years, more inductive approaches have
	emerged, seeking to discover unknown event types and roles in raw text. The
	main recent efforts use probabilistic generative models, as in topic modeling,
	which are formally concise but do not always yield stable or easily
	interpretable results. We argue that event schema induction can benefit from
	greater structure in the process and in linguistic features that distinguish
	words' functions and themes. To maximize our use of limited data, we reverse
	the typical schema induction steps and introduce new similarity measures,
	building an intuitive process for inducing the structure of unknown events.},
  url       = {http://www.aclweb.org/anthology/W17-2710}
}