@inproceedings{miller-etal-2017-unsupervised,
    title = "Unsupervised Domain Adaptation for Clinical Negation Detection",
    author = "Miller, Timothy  and
      Bethard, Steven  and
      Amiri, Hadi  and
      Savova, Guergana",
    editor = "Cohen, Kevin Bretonnel  and
      Demner-Fushman, Dina  and
      Ananiadou, Sophia  and
      Tsujii, Junichi",
    booktitle = "{B}io{NLP} 2017",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada,",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/landing_page/W17-2320/",
    doi = "10.18653/v1/W17-2320",
    pages = "165--170",
    abstract = "Detecting negated concepts in clinical texts is an important part of NLP information extraction systems. However, generalizability of negation systems is lacking, as cross-domain experiments suffer dramatic performance losses. We examine the performance of multiple unsupervised domain adaptation algorithms on clinical negation detection, finding only modest gains that fall well short of in-domain performance."
}