Anonymization of German Legal Court Rulings

In the legal domain, many legal documents such as court decisions and contracts are regularly anonymized. This process requires text sequences with high sensitivity to be identified and neutralized to secure sensitive information from third parties. Usually, this process is performed manually by trained employees. Therefore, anonymization is generally considered an expensive and inefficient process. This work proposes a machine learning approach for the automatic identification of sensitive text elements in German le- gal court decisions and provides an implementation. For this task, different deep neural network architectures based on generally pre- trained contextual embeddings as well as trained word embeddings are evaluated. Because of the lack of non-anonymized data sets, an approach to create pseudonymized data sets is proposed as well.