@InProceedings{duan-he-zhao:2017:I17-1,
  author    = {Duan, Shaoyang  and  He, Ruifang  and  Zhao, Wenli},
  title     = {Exploiting Document Level Information to Improve Event Detection via Recurrent Neural Networks},
  booktitle = {Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)},
  month     = {November},
  year      = {2017},
  address   = {Taipei, Taiwan},
  publisher = {Asian Federation of Natural Language Processing},
  pages     = {352--361},
  abstract  = {This paper tackles the task of event detection, which involves identifying and
	categorizing events. The previous work mainly exist two problems: (1) the
	traditional feature-based methods apply cross-sentence information, yet need
	taking a large amount of human effort to design complicated feature sets and
	inference rules; (2) the representation-based methods though overcome the
	problem of manually extracting features, while just depend on local sentence
	representation. Considering local sentence context is insufficient to resolve
	ambiguities in identifying particular event types, therefore, we propose a
	novel document level Recurrent Neural Networks (DLRNN) model, which can
	automatically extract cross-sentence clues to improve sentence level event
	detection without designing complex reasoning rules. Experiment results show
	that our approach outperforms other state-of-the-art methods on ACE 2005
	dataset without external knowledge base.},
  url       = {http://www.aclweb.org/anthology/I17-1036}
}