@inproceedings{almgren-etal-2016-named,
    title = "Named Entity Recognition in {S}wedish Health Records with Character-Based Deep Bidirectional {LSTM}s",
    author = "Almgren, Simon  and
      Pavlov, Sean  and
      Mogren, Olof",
    editor = "Ananiadou, Sophia  and
      Batista-Navarro, Riza  and
      Cohen, Kevin Bretonnel  and
      Demner-Fushman, Dina  and
      Thompson, Paul",
    booktitle = "Proceedings of the Fifth Workshop on Building and Evaluating Resources for Biomedical Text Mining ({B}io{T}xt{M}2016)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://preview.aclanthology.org/fix-sig-urls/W16-5104/",
    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."
}