@InProceedings{teranishi-shindo-matsumoto:2017:I17-1,
  author    = {Teranishi, Hiroki  and  Shindo, Hiroyuki  and  Matsumoto, Yuji},
  title     = {Coordination Boundary Identification with Similarity and Replaceability},
  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     = {264--272},
  abstract  = {We propose a neural network model for coordination boundary detection. Our
	method relies on the two common properties - similarity and replaceability in
	conjuncts - in order to detect both similar pairs of conjuncts and dissimilar
	pairs of conjuncts. The model improves identification of clause-level
	coordination using bidirectional RNNs incorporating two properties as features.
	We show that our model outperforms the existing state-of-the-art methods on the
	coordination annotated Penn Treebank and Genia corpus without any syntactic
	information from parsers.},
  url       = {http://www.aclweb.org/anthology/I17-1027}
}