@inproceedings{limsopatham-collier-2016-learning,
    title = "Learning Orthographic Features in Bi-directional {LSTM} for Biomedical Named Entity Recognition",
    author = "Limsopatham, Nut  and
      Collier, Nigel",
    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/add_missing_videos/W16-5102/",
    pages = "10--19",
    abstract = "End-to-end neural network models for named entity recognition (NER) have shown to achieve effective performances on general domain datasets (e.g. newswire), without requiring additional hand-crafted features. However, in biomedical domain, recent studies have shown that hand-engineered features (e.g. orthographic features) should be used to attain effective performance, due to the complexity of biomedical terminology (e.g. the use of acronyms and complex gene names). In this work, we propose a novel approach that allows a neural network model based on a long short-term memory (LSTM) to automatically learn orthographic features and incorporate them into a model for biomedical NER. Importantly, our bi-directional LSTM model learns and leverages orthographic features on an end-to-end basis. We evaluate our approach by comparing against existing neural network models for NER using three well-established biomedical datasets. Our experimental results show that the proposed approach consistently outperforms these strong baselines across all of the three datasets."
}