Classification of German court rulings: Detecting the area of law

Ingo Glaser, Florian Matthes

Research output: Contribution to journalConference articlepeer-review


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.

Original languageEnglish
JournalCEUR Workshop Proceedings
StatePublished - 2020
Event4th Workshop on Automated Semantic Analysis of Information in Legal Text, ASAIL 2020 - Virtual, Online
Duration: 9 Dec 2020 → …


  • Area of law detection
  • Legal document classification
  • Natural language processing
  • Semantic analysis of court rulings


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