  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 four 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). In the fourth and last step a
new kind of NER dataset is tested for recognising job titles in texts.
