@InProceedings{judea-strube:2017:I17-1,
  author    = {Judea, Alex  and  Strube, Michael},
  title     = {Event Argument Identification on Dependency Graphs with Bidirectional LSTMs},
  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     = {822--831},
  abstract  = {In this paper we investigate the performance of event argument identification.
	We show that the performance is tied to syntactic complexity. 
	Based on this finding, we propose a novel and effective system for event
	argument identification. Recurrent Neural Networks learn to produce meaningful
	representations of long and short dependency paths. Convolutional Neural
	Networks learn to decompose the lexical context of argument candidates. They
	are combined into a simple system which outperforms a feature-based,
	state-of-the-art event argument identifier without any manual feature
	engineering.},
  url       = {http://www.aclweb.org/anthology/I17-1083}
}