@InProceedings{leeuwenberg-moens:2017:EACLlong,
  author    = {Leeuwenberg, Artuur  and  Moens, Marie-Francine},
  title     = {Structured Learning for Temporal Relation Extraction from Clinical Records},
  booktitle = {Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers},
  month     = {April},
  year      = {2017},
  address   = {Valencia, Spain},
  publisher = {Association for Computational Linguistics},
  pages     = {1150--1158},
  abstract  = {We propose a scalable structured learning model that jointly predicts temporal
	relations between events and temporal expressions (TLINKS), and the relation
	between these events and the document creation time (DCTR). We employ a
	structured perceptron, together with integer linear programming constraints for
	document-level inference during training and prediction to exploit relational
	properties of temporality, together with global learning of the relations at
	the document level. Moreover, this study gives insights in the results of
	integrating constraints for temporal relation extraction when using structured
	learning and prediction. Our best system outperforms the state-of-the art on
	both the CONTAINS TLINK task, and the DCTR task.},
  url       = {http://www.aclweb.org/anthology/E17-1108}
}