Abstract
The human resource (HR) domain contains various types of privacy-sensitive textual data, such as e-mail correspondence and performance appraisal. Doing research on these documents brings several challenges, one of them anonymisation. In this paper, we evaluate the current Dutch text de-identification methods for the HR domain in three steps. First, by updating one of these methods with the latest named entity recognition (NER) models. The result is that the NER model based on the CoNLL 2002 corpus in combination with the BERTje transformer give the best combination for suppressing persons (recall 0.94) and locations (recall 0.82). For suppressing gender, DEDUCE is performing best (recall 0.53). Second NER evaluation is based on both strict de-identification of entities (a person must be suppressed as a person) and third evaluation on a loose sense of de-identification (no matter what how a person is suppressed, as long it is suppressed).
Original language | English |
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Title of host publication | 10th International Conference on Advances in Computing and Information Technology (ACITY 2020), November 28~29, 2020, London, United Kingdom |
Editors | David C. Wyld, Dhinaharan Nagamalai |
Publisher | AIRCC Publishing Corporation |
Pages | 239–249 |
Volume | 10 |
ISBN (Print) | 978-1-925953-29-9 |
DOIs | |
Publication status | Published - Nov 2020 |
Bibliographical note
Volume 10, Number 15, November 2020Keywords
- Named Entity Recognition
- Dutch
- NER
- BERT
- evaluation
- de-identification