@InProceedings{long-EtAl:2017:SemEval,
  author    = {Long, Yu  and  Li, Zhijing  and  Wang, Xuan  and  Li, Chen},
  title     = {XJNLP at SemEval-2017 Task 12: Clinical temporal information ex-traction with a Hybrid Model},
  booktitle = {Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)},
  month     = {August},
  year      = {2017},
  address   = {Vancouver, Canada},
  publisher = {Association for Computational Linguistics},
  pages     = {1014--1018},
  abstract  = {Temporality is crucial in understanding the course of clinical events from a
	patient’s electronic health recordsand temporal processing is becoming more
	and more important for improving access to content.SemEval 2017 Task 12
	(Clinical TempEval) addressed this challenge using the THYME corpus, a corpus
	of clinical narratives annotated with a schema based on TimeML2 guidelines. We
	developed and evaluated approaches for: extraction of temporal expressions
	(TIMEX3) and EVENTs; EVENT attributes; document-time relations. Our approach is
	a hybrid model which is based on rule based methods, semi-supervised learning,
	and semantic features with addition of manually crafted rules.},
  url       = {http://www.aclweb.org/anthology/S17-2178}
}