@InProceedings{lamurias-EtAl:2017:SemEval,
  author    = {Lamurias, Andre  and  Sousa, Diana  and  Pereira, Sofia  and  Clarke, Luka  and  Couto, Francisco M},
  title     = {ULISBOA at SemEval-2017 Task 12: Extraction and classification of temporal expressions and events},
  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     = {1019--1023},
  abstract  = {This paper presents our approach to participate in the SemEval 2017 Task 12:
	Clinical TempEval challenge, specifically in the event and time expressions
	span and attribute identification subtasks (ES, EA, TS, TA).
	  Our approach consisted in training Conditional Random Fields (CRF)
	classifiers using the provided annotations, and in creating manually curated
	rules to classify the attributes of each event and time expression.
	  We used a set of common features for the event and time CRF classifiers, and
	a set of features specific to each type of entity, based on domain knowledge.
	  Training only on the source domain data, our best F-scores were 0.683 and
	0.485 for event and time span identification subtasks.
	  When adding target domain annotations to the training data, the best F-scores
	obtained were 0.729 and 0.554, for the same subtasks.
	  We obtained the second highest F-score of the challenge on the event polarity
	subtask (0.708).
	  The source code of our system, Clinical Timeline Annotation (CiTA), is
	available at https://github.com/lasigeBioTM/CiTA.},
  url       = {http://www.aclweb.org/anthology/S17-2179}
}