@inproceedings{lange-etal-2020-closing,
    title = "Closing the Gap: Joint De-Identification and Concept Extraction in the Clinical Domain",
    author = {Lange, Lukas  and
      Adel, Heike  and
      Str{\"o}tgen, Jannik},
    editor = "Jurafsky, Dan  and
      Chai, Joyce  and
      Schluter, Natalie  and
      Tetreault, Joel",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/Add-Cong-Liu-Florida-Atlantic-University-author-id/2020.acl-main.621/",
    doi = "10.18653/v1/2020.acl-main.621",
    pages = "6945--6952",
    abstract = "Exploiting natural language processing in the clinical domain requires de-identification, i.e., anonymization of personal information in texts. However, current research considers de-identification and downstream tasks, such as concept extraction, only in isolation and does not study the effects of de-identification on other tasks. In this paper, we close this gap by reporting concept extraction performance on automatically anonymized data and investigating joint models for de-identification and concept extraction. In particular, we propose a stacked model with restricted access to privacy sensitive information and a multitask model. We set the new state of the art on benchmark datasets in English (96.1{\%} F1 for de-identification and 88.9{\%} F1 for concept extraction) and Spanish (91.4{\%} F1 for concept extraction)."
}