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The human resource (HR) domain contains various types of privacy-sensitivetextual data, such as e-mail correspondence and performance appraisal. Doingresearch on these documents brings several challenges, one of themanonymisation. In this paper, we evaluate the current Dutch textde-identification methods for the HR domain in four steps. First, by updatingone of these methods with the latest named entity recognition (NER) models. Theresult is that the NER model based on the CoNLL 2002 corpus in combination withthe BERTje transformer give the best combination for suppressing persons(recall 0.94) and locations (recall 0.82). For suppressing gender, DEDUCE isperforming best (recall 0.53). Second NER evaluation is based on both strictde-identification of entities (a person must be suppressed as a person) andthird evaluation on a loose sense of de-identification (no matter what how aperson is suppressed, as long it is suppressed). In the fourth and last step anew kind of NER dataset is tested for recognising job titles in texts.

Dutch Named Entity Recognition and De-identification Methods for the Human Resource Domain