Research Center for Digital Sustainability

Topic Modeling for Swiss Court Rulings

Topic Modeling for Swiss Court Rulings

This project is available for Seminar students.


Swiss court decisions are anonymized to protect the privacy of the involved people (parties, victims, etc.). Previous research [1] has shown that it is possible to re-identify companies involved in court decisions by linking the rulings with external data in certain cases. Our project tries to further build an automated system for re-identifying involved people from court rulings. This system can then be used as a test for the anonymization practice of Swiss courts. For more information regarding the overarching research project please go here. We use methods of Natural Language Processing (NLP), a subfield of Artificial Intelligence (AI), Linguistics, and Computer Science.

Topic Modeling is a text-mining tool used to capture hidden semantics in texts. It clusters documents into topics based on similar words. Eventually, the topics discovered may guide the search for external data required for the re-identification of a given court ruling.

Research Questions

So far, Swiss court decisions have not been analyzed for their abstract topics.

RQ1: Which topic model provides the most coherent topics on Swiss court rulings?

RQ2: What conclusions can be drawn from the topic model and its visualizations?


  1. Preprocess the data (Swiss German Federal Court Rulings)
  2. Build the topic model (e.g. with BERTopic or contextualized-topic-models)
  3. Experiment with different topic models (LDA, NMF, BERTopic etc.), different numbers of topics, and other hyperparameters
  4. Visualize the results (e.g. with lda-vis)
  5. Evaluate the results


⬤⬤⬤⬤◯ Programming

⬤⬤⬤⬤◯ Experimentation

⬤◯◯◯◯ Literature


Good programming skills (preferably in Python)

Preferably experience with Machine Learning


Joel Niklaus


[1] Vokinger, K.N., Mühlematter, U.J., 2019. Re-Identifikation von Gerichtsurteilen durch «Linkage» von Daten(banken). Jusletter 27.