Dávid Varga

The increasing availability of legal documentation provides opportunities for applying empirical research methods that enable a comprehensive analysis of complex legal data. Systematic Content Analysis offers a structured approach for examining legal texts. This paper examines the possibility of anomaly detection in legal text through the employment of machine learning methods. The conclusions presented are formulated on a basis of a case study focusing on contractual agreements concluded between public authorities and private service providers in the field of cybersecurity. In this regard, we propose an approach to the detection of anomalous legal text, providing a categorization of attributes that impact the determination of anomalies and describing their significance in the selected domain.

This paper presents preliminary methods and results on extracting keyphrases from Slovak Supreme Court decisions to be used in the future by a recommender system for expert annotators employed by the court. We developed methods using referenced law articles and court decisions to improve traditional statistical and semantic approaches for keyphrase extraction. These methods were evaluated using a manually annotated dataset, demonstrating that incorporating extracted references enhances performance, achieving an F1 score percentage of 86.35%.