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        <title>Closing the Gap: Joint De-Identification and Concept Extraction in the Clinical Domain</title>
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        <namePart type="given">Lukas</namePart>
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            <namePart type="given">Dan</namePart>
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    <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).</abstract>
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%0 Conference Proceedings
%T Closing the Gap: Joint De-Identification and Concept Extraction in the Clinical Domain
%A Lange, Lukas
%A Adel, Heike
%A Strötgen, Jannik
%Y Jurafsky, Dan
%Y Chai, Joyce
%Y Schluter, Natalie
%Y Tetreault, Joel
%S Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F lange-etal-2020-closing
%X 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).
%R 10.18653/v1/2020.acl-main.621
%U https://aclanthology.org/2020.acl-main.621
%U https://doi.org/10.18653/v1/2020.acl-main.621
%P 6945-6952

Markdown (Informal)

[Closing the Gap: Joint De-Identification and Concept Extraction in the Clinical Domain](https://aclanthology.org/2020.acl-main.621) (Lange et al., ACL 2020)

• Closing the Gap: Joint De-Identification and Concept Extraction in the Clinical Domain (Lange et al., ACL 2020)

ACL

• Lukas Lange, Heike Adel, and Jannik Strötgen. 2020. Closing the Gap: Joint De-Identification and Concept Extraction in the Clinical Domain. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 6945–6952, Online. Association for Computational Linguistics.