Name
Title | ||
|---|---|---|
Closing the Gap: Joint De-Identification and Concept Extraction in the Clinical Domain |
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). |
Year | Venue | DocType |
|---|---|---|
2020 | ACL | Conference |
Volume | Citations | PageRank |
2020.acl-main | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lukas Lange | 1 | 0 | 2.03 |
| Heike Adel | 2 | 62 | 15.03 |
| Jannik Strötgen | 3 | 492 | 38.20 |