Artificial Intelligence and Law

, Volume 15, Issue 1, pp 1–17 | Cite as

Automatic Classification of Provisions in Legislative Texts



Legislation usually lacks a systematic organization which makes the management and the access to norms a hard problem to face. A more analytic semantic unit of reference (provision) for legislative texts was identified. A model of provisions (provisions types and their arguments) allows to describe the semantics of rules in legislative texts. It can be used to develop advanced semantic-based applications and services on legislation. In this paper an automatic bottom-up strategy to qualify existing legislative texts in terms of provision types is described.


model of provisions Naïve Bayes classifier SVM classifier text categorization 


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  1. Apté C., Damerau F. J., Weiss S. W. (1994) Automated Learning of Decision Rules for Text Categorization. ACM Transactions on Information Systems 12(3):233–251CrossRefGoogle Scholar
  2. Bartolini, R., Lenci, A., Montemagni, S., Pirrelli, V., and Soria, C. (2004). Automatic Classification and Analysis of Provisions in Italian Legal Texts: A Case Study. In Proceedings of the Second International Workshop on Regulatory Ontologies.Google Scholar
  3. Biagioli, C. (1991). Definitional Elements of a Language for Representation of Statutory. Rechtstheorie 11.Google Scholar
  4. Biagioli, C. (1992). Law Making Environment. In Proceedings of Workshop on Legal Knowledge and Legal Reasoning Systems, Tokyo.Google Scholar
  5. Biagioli, C. (1997). Towards a Legal Rules Functional Micro-ontology. In Proceedings of Workshop LEGONT ’97.Google Scholar
  6. Biagioli, C., and Francesconi, E. (2005). A Semantics-based Visual Framework for Planning a New Bill. In Proceedings of the Jurix Conference: Legal Knowledge and Information Systems, 103–104.Google Scholar
  7. Biagioli, C., Francesconi, E., Passerini, A., Montemagni, S., and Soria, C. (2005a). Automatic Semantics Extraction in Law Documents. In Proceedings of International Conference on Artificial Intelligence and Law, 133–139.Google Scholar
  8. Biagioli, C., Francesconi, E., Spinosa, P., and Taddei, M. (2005b). A Legal Drafting Environment Based on Formal and Semantic XML Standards. In Proceedings of International Conference on Artificial Intelligence and Law, 244–245.Google Scholar
  9. Biagioli, C., and Turchi, F. (2005). Model and Ontology based Conceptual Searching in Legislative XML Collections. In: Proceedings of the Workshop on Legal Ontologies and Artificial Intelligence Techniques, 83–89.Google Scholar
  10. Bishop C. (1995) Neural Networks for Pattern Recognition. Oxford, Oxford University PressGoogle Scholar
  11. Boer, A., Hoekstra, R., and Winkels, R. (2002). MetaLex: Legislation in XML. In Proceedings of JURIX 2002: Legal Knowledge and Information System, 1–10.Google Scholar
  12. C. Buckley, G. Salton (1988) Term-weighting Approaches in Automatic Text Retrieval. Information Processing and Management 24(5):513–523CrossRefGoogle Scholar
  13. Burges, C. (1998). A Tutorial on Support Vector Machines for Pattern Recognition. In Data Mining and Knowledge Discovery, Vol. 2, Kluwer Academic Publishers: Boston.Google Scholar
  14. Cortes C., Vapnik V. (1995) Support Vector Networks. Machine Learning 20:1–25Google Scholar
  15. Crammer K., Singer Y. (2002) On the Algorithmic Implementation of Multiclass Kernel-based Vector Machines. Journal on Machine Learning Research 2:265–292MATHCrossRefGoogle Scholar
  16. Dumais, S., Platt, J., Heckerman, D., and Sahami, M. (1998). Inductive Learning Algorithms and Representations for Text Categorization. In CIKM ’98: Proceedings of the Seventh International Conference on Information and Knowledge Management, 148–155, ACM Press: New York, NY, USA.Google Scholar
  17. Hsu, C.-W., and Lin, C.-J. (2002). A Comparison of Methods for Multi-class Support Vector Machines. IEEE Transactions on Neural Networks 13(2): 415–425.Google Scholar
  18. Jensen, F. (1996). Introduction to Bayesian Networks. Springer-Verlag.Google Scholar
  19. Joachims, T. (1997). A Probabilistic Analysis of the Rocchio Algorithm with TFIDF for Text Categorization. In Proceedings of the Fourteenth International Conference on Machine Learning, 143–151, Morgan Kaufmann Publishers Inc.Google Scholar
  20. Lee, Y., Lin, Y., and Wahba, G. (2001). Multicategory Support Vector Machines. Technical Report 1043, Dept. of Statistics, University of Wisconsin.Google Scholar
  21. Lewis, D. (1992). Automating the Construction of Internet Portals with Machine Learning. In␣Proceedings of ACM International Conference on Research and Development in Information Retrieval, 37–50.Google Scholar
  22. Megale F., Vitali F. (2001) I DTD dei documenti di Norme in Rete. Informatica e Diritto 1:167–231Google Scholar
  23. Ng A., Jordan M. (2002) On Discriminative vs Generative Classifiers: A Comparison of Logistic Regression and Naive Bayes. In: Dietterich T., Becker S., Ghahramani Z. (eds), Advances in Neural Information Processing Systems 14. Cambridge, MA, MIT PressGoogle Scholar
  24. Palmirani, M. (2005). Time Model in Normative Information System. In Post-proceedings of the ICAIL Workshop on the Role of Legal Knowledge in e-Government.Google Scholar
  25. Passerini, A. (2004). Kernel Methods, Multiclass Classification and Applications to Computational Molecular Biology. Ph.D. thesis, Università di Firenze: Italy.Google Scholar
  26. Quinlan J. (1986) Inductive Learning of Decision Trees. Machine Learning 1:81–106Google Scholar
  27. Raz J. (1977) Il Concetto di Sistema Giuridico. Il Mulino, BolognaGoogle Scholar
  28. Schölkopf B., Smola A. (2002) Learning with Kernels. Cambridge, MA, The MIT PressGoogle Scholar
  29. Sebastiani F. (2002) Machine Learning in Automated Text Categorization. ACM Computing Surveys 34(1):1–47CrossRefGoogle Scholar
  30. Shawe-Taylor, J., and Cristianini, N. (2004). Kernel Methods for Pattern Analysis. Cambridge University Press.Google Scholar
  31. Spinosa, P. (2001). Identification of Legal Documents through URNs (Uniform Resource Names). In Proceedings of the EuroWeb 2001, The Web in Public Administration.Google Scholar
  32. Vapnik V. (1998) Statistical Learning Theory. New York, WileyMATHGoogle Scholar
  33. Weston, J., and Watkins, C. (1998). Multi-class Support Vector Machines. Technical Report CSD-TR-98-04, Royal Holloway, University of London, Department of Computer Science.Google Scholar
  34. Yang, Y., and Pedersen, J. (1997). A Comparative Study on Feature Selection in Text Categorization. In Proceedings of the Fourteenth International Conference on Machine Learning, 412–420, Morgan Kaufmann Publishers Inc.Google Scholar

Copyright information

© Springer Science+Business Media B.V. 2007

Authors and Affiliations

  1. 1.ITTIG - CNR, Istituto di Teoria e Tecniche dell’Informazione Giuridica - Consiglio Nazionale delle RicercheFlorenceItaly
  2. 2.DSI - Dipartimento di Sistemi e InformaticaUniversità di FirenzeFlorenceItaly

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