Automatic Anonymization of Swiss Federal Supreme Court Rulings

Niklaus, Joël; Mamié, Robin; Stürmer, Matthias; Brunner, Daniel; Gygli, Marcel (2023). Automatic Anonymization of Swiss Federal Supreme Court Rulings In: Proceedings of the Natural Legal Language Processing Workshop 2023 (pp. 159-165). Stroudsburg, PA, USA: Association for Computational Linguistics 10.18653/v1/2023.nllp-1.16

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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 indomain data. Our results show that using indomain 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.

Item Type:	Conference or Workshop Item (Paper)
Division/Institute:	Business School > Institute for Public Sector Transformation Business School > Institute for Public Sector Transformation > Digital Sustainability Lab Business School
Name:	Niklaus, Joël0000-0002-2779-1653; Mamié, Robin; Stürmer, Matthias0000-0001-9038-4041; Brunner, Daniel and Gygli, Marcel
Publisher:	Association for Computational Linguistics
Language:	English
Submitter:	Marcel Gygli
Date Deposited:	31 Jul 2024 07:52
Last Modified:	31 Jul 2024 09:29
Publisher DOI:	10.18653/v1/2023.nllp-1.16
Related URLs:	Publication
ARBOR DOI:	10.24451/arbor.22003
URI:	https://arbor.bfh.ch/id/eprint/22003