@InProceedings{almgren-pavlov-mogren:2016:BioTxtM2016,
  author    = {Almgren, Simon  and  Pavlov, Sean  and  Mogren, Olof},
  title     = {Named Entity Recognition in Swedish Health Records with Character-Based Deep Bidirectional LSTMs},
  booktitle = {Proceedings of the Fifth Workshop on Building and Evaluating Resources for Biomedical Text Mining (BioTxtM2016)},
  month     = {December},
  year      = {2016},
  address   = {Osaka, Japan},
  publisher = {The COLING 2016 Organizing Committee},
  pages     = {30--39},
  abstract  = {We propose an approach for named entity recognition in medical data, using a
	character-based
	deep bidirectional recurrent neural network. Such models can learn features and
	patterns based
	on the character sequence, and are not limited to a fixed vocabulary. This
	makes them very well
	suited for the NER task in the medical domain. Our experimental evaluation
	shows promising
	results, with a 60% improvement in F 1 score over the baseline, and our system
	generalizes well
	between different datasets.},
  url       = {http://aclweb.org/anthology/W16-5104}
}