@InProceedings{ning-feng-roth:2017:EMNLP2017,
  author    = {Ning, Qiang  and  Feng, Zhili  and  Roth, Dan},
  title     = {A Structured Learning Approach to Temporal Relation Extraction},
  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     = {1027--1037},
  abstract  = {Identifying temporal relations between events is an essential step towards
	natural language understanding. However, the temporal relation between two
	events in a story depends on, and is often dictated by, relations among other
	events. Consequently, effectively identifying temporal relations between events
	is a challenging problem even for human annotators. This paper suggests that it
	is important to take these dependencies into account while learning to identify
	these relations and proposes a structured learning approach to address this
	challenge. As a byproduct, this provides a new perspective on handling missing
	relations, a known issue that hurts existing methods. As we show, the proposed
	approach results in significant improvements on the two commonly used data sets
	for this problem.},
  url       = {https://www.aclweb.org/anthology/D17-1108}
}