Research Center for Digital Sustainability

Topic Modeling for Swiss Court Rulings

Topic Modeling for Swiss Court Rulings

This project is available for both Bachelor's and Master's students.

Introduction

Swiss court decisions are anonymized to protect the privacy of the involved people (parties, victims, etc.). Previous research [1] has shown that in certain cases it is possible to re-identify companies involved in court decisions by linking the rulings with external data. Our project tries to go a step further by building 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.

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

Swiss court decisions have never 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?

Steps

  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

Activities

⬤⬤⬤⬤◯ Programming

⬤⬤⬤⬤◯ Experimentation

⬤◯◯◯◯ Literature

Prerequisites

Good programming skills (preferably in Python)

Preferably experience with Machine Learning

Contact

Joel Niklaus

References

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