@inproceedings{meripo-konam-2021-extracting,
    title = "Extracting Appointment Spans from Medical Conversations",
    author = "Meripo, Nimshi Venkat  and
      Konam, Sandeep",
    editor = "Shivade, Chaitanya  and
      Gangadharaiah, Rashmi  and
      Gella, Spandana  and
      Konam, Sandeep  and
      Yuan, Shaoqing  and
      Zhang, Yi  and
      Bhatia, Parminder  and
      Wallace, Byron",
    booktitle = "Proceedings of the Second Workshop on Natural Language Processing for Medical Conversations",
    month = jun,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2021.nlpmc-1.6/",
    doi = "10.18653/v1/2021.nlpmc-1.6",
    pages = "41--46",
    abstract = "Extracting structured information from medical conversations can reduce the documentation burden for doctors and help patients follow through with their care plan. In this paper, we introduce a novel task of extracting appointment spans from medical conversations. We frame this task as a sequence tagging problem and focus on extracting spans for appointment reason and time. However, annotating medical conversations is expensive, time-consuming, and requires considerable domain expertise. Hence, we propose to leverage weak supervision approaches, namely incomplete supervision, inaccurate supervision, and a hybrid supervision approach and evaluate both generic and domain-specific, ELMo, and BERT embeddings using sequence tagging models. The best performing model is the domain-specific BERT variant using weak hybrid supervision and obtains an F1 score of 79.32."
}