Exploiting co-occurrence networks for classification of implicit inter-relationships in legal texts

Abstract The interpretation of any legal norm typically requires consideration of relationships between parts within the same piece of legislation. This work describes a general framework for the development of a system to identify and classify implicit inter-relationships between parts of a legal text. In particular, our approach demonstrates the usefulness of co-occurrence networks of terms, in a practical experimental setting based on an EU Regulation. First, a manual annotation task identify instances of different kinds of implicit links in the norm. In addition to a typical NLP pipeline, our framework includes a technique from Information Architecture, i.e. card sorting. Second, we construct co-occurrence networks of the law terms to derive graph metrics. Third, binary classification experiments identify the existence (and the type) of inter-relationships by using a Bag-of-Ngrams model integrated with network analysis features. The results demonstrate how the adoption of co-occurrence network features improves the identification of links, for all the classifiers here considered. This is encouraging toward a wider adoption of this kind of network analysis technique in legal informatics.