@InProceedings{macavaney-cohan-goharian:2017:SemEval,
  author    = {MacAvaney, Sean  and  Cohan, Arman  and  Goharian, Nazli},
  title     = {GUIR at SemEval-2017 Task 12: A Framework for Cross-Domain Clinical Temporal Information Extraction},
  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     = {1024--1029},
  abstract  = {Clinical TempEval 2017 (SemEval 2017 Task 12) addresses the task of
	cross-domain temporal extraction from clinical text. We present a system for
	this task that uses supervised learning for the extraction of temporal
	expression and event spans with corresponding attributes and narrative
	container relations. Approaches include conditional random fields and decision
	tree ensembles, using lexical, syntactic, semantic, distributional, and
	rule-based features. Our system received best or second best scores in TIMEX3
	span, EVENT span, and CONTAINS relation extraction.},
  url       = {http://www.aclweb.org/anthology/S17-2180}
}