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

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. (C)& nbsp;2021 Elsevier Ltd. All rights reserved.

Automated summary

This paper describes a general framework for identifying and classifying implicit inter-relationships within legal texts. Through experiments, the usefulness of co-occurrence networks of terms is demonstrated, and a model integrating network analysis features is proposed to identify the type of relationships. The results show that the adoption of co-occurrence network features improves relationship identification.