@InProceedings{huang-kurohashi:2017:EventStory,
  author    = {Huang, Yin Jou  and  Kurohashi, Sadao},
  title     = {Improving Shared Argument Identification in Japanese Event Knowledge Acquisition},
  booktitle = {Proceedings of the Events and Stories in the News Workshop},
  month     = {August},
  year      = {2017},
  address   = {Vancouver, Canada},
  publisher = {Association for Computational Linguistics},
  pages     = {21--30},
  abstract  = {Event knowledge represents the knowledge of causal and temporal relations
	between events. Shared arguments of event knowledge encode patterns of role
	shifting in successive events. A two-stage framework was proposed for the task
	of Japanese event knowledge acquisition, 
	in which related event pairs are first extracted, and shared arguments are then
	identified to form the complete event knowledge. This paper focuses on the
	second stage of this framework, and proposes a method to improve the shared
	argument identification of related event pairs. We constructed a gold dataset
	for shared argument learning. By evaluating our system on this gold dataset, we
	found that our proposed model outperformed the baseline models by a large
	margin.},
  url       = {http://www.aclweb.org/anthology/W17-2704}
}