@InProceedings{dai-yao-huang:2017:I17-2,
  author    = {Dai, Zeyu  and  Yao, Wenlin  and  Huang, Ruihong},
  title     = {Using Context Events in Neural Network Models for Event Temporal Status Identification},
  booktitle = {Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)},
  month     = {November},
  year      = {2017},
  address   = {Taipei, Taiwan},
  publisher = {Asian Federation of Natural Language Processing},
  pages     = {234--239},
  abstract  = {Focusing on the task of identifying event temporal status, we find that events
	directly or indirectly governing the target event in a dependency tree are most
	important contexts. Therefore, we extract dependency chains containing context
	events and use them as input in neural network models, which consistently
	outperform previous models using local context words as input. Visualization
	verifies that the dependency chain representation can effectively capture the
	context events which are closely related to the target event and play key roles
	in predicting event temporal status.},
  url       = {http://www.aclweb.org/anthology/I17-2040}
}