Closing the Gap: Joint De-Identification and Concept Extraction in the Clinical Domain

Lukas Lange, Heike Adel, Jannik Strötgen


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).
Anthology ID:
2020.acl-main.621
Volume:
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
Month:
July
Year:
2020
Address:
Online
Editors:
Dan Jurafsky, Joyce Chai, Natalie Schluter, Joel Tetreault
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
6945–6952
Language:
URL:
https://aclanthology.org/2020.acl-main.621
DOI:
10.18653/v1/2020.acl-main.621
Bibkey:
Cite (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.
Cite (Informal):
Closing the Gap: Joint De-Identification and Concept Extraction in the Clinical Domain (Lange et al., ACL 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.acl-main.621.pdf
Video:
 http://slideslive.com/38928788
Code
 boschresearch/joint_anonymization_extraction