Exploring network analysis in a corpus-based approach to legal texts: A case study

Automatic analysis of legal texts is increasingly of interest to address the issue of interpretation and compliance concerns. This paper proposes a two-step framework to investigate implicit relationships in legal documents, starting with a corpus-based approach. By introducing an annotation process, the goal is to obtain a gold standard corpus suitable for machine learning experiments. In a second step, we propose a set of features to perform the task of predicting relationships between parts of a norm, as a way to improve legal interpretation. We discuss our first results concerning the annotation task, as well as the adoption of graph-based measures derived from social network analysis. We perform a practical application to an European Union regulation. The proposed framework exploiting network analysis in addition to a corpus-based approach can be applied to address a binary classification task.