@inproceedings{suster-etal-2018-revisiting,
    title = "Revisiting neural relation classification in clinical notes with external information",
    author = "{\v{S}}uster, Simon  and
      Sushil, Madhumita  and
      Daelemans, Walter",
    editor = "Lavelli, Alberto  and
      Minard, Anne-Lyse  and
      Rinaldi, Fabio",
    booktitle = "Proceedings of the Ninth International Workshop on Health Text Mining and Information Analysis",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/W18-5603/",
    doi = "10.18653/v1/W18-5603",
    pages = "22--28",
    abstract = "Recently, segment convolutional neural networks have been proposed for end-to-end relation extraction in the clinical domain, achieving results comparable to or outperforming the approaches with heavy manual feature engineering. In this paper, we analyze the errors made by the neural classifier based on confusion matrices, and then investigate three simple extensions to overcome its limitations. We find that including ontological association between drugs and problems, and data-induced association between medical concepts does not reliably improve the performance, but that large gains are obtained by the incorporation of semantic classes to capture relation triggers."
}