  Releasing court decisions to the public relies on proper anonymization to
protect all involved parties, where necessary. The Swiss Federal Supreme Court
relies on an existing system that combines different traditional computational
methods with human experts. In this work, we enhance the existing anonymization
software using a large dataset annotated with entities to be anonymized. We
compared BERT-based models with models pre-trained on in-domain data. Our
results show that using in-domain data to pre-train the models further improves
the F1-score by more than 5\% compared to existing models. Our work
demonstrates that combining existing anonymization methods, such as regular
expressions, with machine learning can further reduce manual labor and enhance
automatic suggestions.
