Classification of German Court Rulings - Detecting the Area of Law
This paper investigates on the feasibility of automatically detecting the legal area of court rulings. Hereby, we establish the hypothesis that the allocation to a field of law is often ambiguous and errors occur in that process as a result. A dataset constituting over 9.000 labelled court rulings was used in order to train different machine learning (ML) classifiers. Additionally, we applied rule-based approaches utilizing domain knowledge of legal experts. Our models outperformed the rule-based approaches significantly. Hence, we could show that the performance of ML models are less prone to errors than the manual assignment of legal experts.
| Attribute | Value |
|---|---|
| Address | Virtual |
| Authors | Ingo Glaser , Prof. Dr. Florian Matthes |
| Citation | Glaser, I.; Matthes, F.: Classification of German Court Rulings: Detecting the Area of Law, ASAIL: Automated Semantic Analysis of Information in Legal Text, Virtual, 2020 |
| Key | Gl20c |
| Research project | Semantic Analysis of Court Rulings |
| Title | Classification of German Court Rulings: Detecting the Area of Law |
| Type of publication | Workshop |
| Year | 2020 |
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