@InProceedings{choubey-huang:2017:EMNLP20171,
  author    = {Choubey, Prafulla Kumar  and  Huang, Ruihong},
  title     = {A Sequential Model for Classifying Temporal Relations between Intra-Sentence Events},
  booktitle = {Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  pages     = {1796--1802},
  abstract  = {We present a sequential model for temporal relation classification between
	intra-sentence events. The key observation is that the overall syntactic
	structure and compositional meanings of the multi-word context between events
	are important for distinguishing among fine-grained temporal relations.
	Specifically, our approach first extracts a sequence of context words that
	indicates the temporal relation between two events, which well align with the
	dependency path between two event mentions. The context word sequence, together
	with a parts-of-speech tag sequence and a dependency relation sequence that are
	generated corresponding to the word sequence, are then provided as input to
	bidirectional recurrent neural network (LSTM) models. The neural nets learn
	compositional syntactic and semantic representations of contexts surrounding
	the two events and predict the temporal relation between them. Evaluation of
	the proposed approach on TimeBank corpus shows that sequential modeling is
	capable of accurately recognizing temporal relations between events, which
	outperforms a neural net model using various discrete features as input that
	imitates previous feature based models.},
  url       = {https://www.aclweb.org/anthology/D17-1190}
}