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
- 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)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2020.acl-main.621.pdf
- Code
- boschresearch/joint_anonymization_extraction