@InProceedings{jochim-deleris:2017:EACLlong,
  author    = {Jochim, Charles  and  Deleris, Lea},
  title     = {Named Entity Recognition in the Medical Domain with Constrained CRF Models},
  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     = {839--849},
  abstract  = {This paper investigates how to improve performance on information
	  extraction tasks by constraining and sequencing CRF-based
	  approaches.  We consider two different relation extraction tasks,
	  both from the medical literature: dependence relations and
	  probability statements.  We explore whether adding constraints can
	  lead to an improvement over standard CRF decoding.  Results on our
	  relation extraction tasks are promising, showing significant
	  increases in performance from both (i) adding constraints to
	  post-process the output of a baseline CRF, which captures ``domain
	  knowledge'', and (ii) further allowing flexibility in the
	  application of those constraints by leveraging a binary classifier
	  as a pre-processing step.},
  url       = {http://www.aclweb.org/anthology/E17-1079}
}