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Artificial Intelligence and Law

, Volume 27, Issue 2, pp 199–225 | Cite as

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

  • Rohan NandaEmail author
  • Giovanni Siragusa
  • Luigi Di Caro
  • Guido Boella
  • Lorenzo Grossio
  • Marco Gerbaudo
  • Francesco Costamagna
Article
  • 211 Downloads

Abstract

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.

Keywords

Text similarity Transposition Machine learning 

Notes

Acknowledgements

Research presented in this paper is conducted as a Ph.D. research at the University of Turin, within the Erasmus Mundus Joint International Doctoral (Ph.D.) programme in Law, Science and Technology. This work has been partially supported by the European Union’s Horizon 2020 research and innovation programme under the Marie Skodowska-Curie Grant agreement no. 690974 for the project “MIREL: MIning and REasoning with Legal texts”.

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Copyright information

© Springer Nature B.V. 2018

Authors and Affiliations

  1. 1.Department of Computer ScienceUniversity of TurinTurinItaly
  2. 2.Department of LawUniversity of TurinTurinItaly

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