Unsupervised and supervised text similarity systems for automated identification of national implementing measures of European directives

The automated identification of national implementations (NIMs) of European directives by text similarity techniques has shown promising preliminary results. Previous works have proposed and utilized unsupervised lexical and semantic similarity techniques based on vector space models, latent semantic analysis and topic models. However, these techniques were evaluated on a small multilingual corpus of directives and NIMs. In this paper, we utilize word and paragraph embedding models learned by shallow neural networks from a multilingual legal corpus of European directives and national legislation (from Ireland, Luxembourg and Italy) to develop unsupervised semantic similarity systems to identify transpositions. We evaluate these models and compare their results with the previous unsupervised methods on a multilingual test corpus of 43 Directives and their corresponding NIMs. We also develop supervised machine learning models to identify transpositions and compare their performance with different feature sets

Towards legal compliance by correlating Standards and Laws with a semi-automated methodology

Since generally legal regulations do not provide clear parameters to determine when their requirements are met, achieving legal compliance is not trivial. The adoption of standards could help create an argument of compliance in favour of the implementing party, provided there is a clear correspondence between the provisions of a specific standard and the regulation's requirements. However, identifying such correspondences is a complex process which is complicated further by the fact that the established correlations may be overridden in time e.g., because newer court decisions change the interpretation of certain legal provisions. To help solve these problems, we present a framework that supports legal experts in recognizing correlations between provisions in a standard and requirements in a given law. The framework relies on state-of-the-art Natural Language Semantics techniques to process the linguistic terms of the two documents, and maintains a knowledge base of the logic representations of the terms, together with their defeasible correlations, both formal and substantive. An application of the framework is shown by comparing a provision of the European General Data Protection Regulation with the ISO/IEC 27018:2014 standard