Advertisement

Automatic Classification and Analysis of Provisions in Italian Legal Texts: A Case Study

  • Roberto Bartolini
  • Alessandro Lenci
  • Simonetta Montemagni
  • Vito Pirrelli
  • Claudia Soria
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3292)

Abstract

In this paper we address the problem of automatically enriching legal texts with semantic annotation, an essential pre–requisite to effective indexing and retrieval of legal documents. This is done through illustration of SALEM (Semantic Annotation for LEgal Management), a computational system developed for automated semantic annotation of (Italian) law texts. SALEM is an incremental system using Natural Language Processing techniques to perform two tasks: i) classify law paragraphs according to their regulatory content, and ii) extract relevant text fragments corresponding to specific semantic roles that are relevant for the different types of regulatory content. The paper sketches the overall architecture of SALEM and reports results of a preliminary case study on a sample of Italian law texts.

Keywords

Semantic Role Semantic Annotation Legal Text Commission Decision Legal Domain 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Bartolini, R., Lenci, A., Montemagni, S., Pirrelli, V.: The lexicon-grammar balance in robust parsing of Italian. In: Proc. of 3rd International Conference on Language Resources and Evaluation (2002)Google Scholar
  2. 2.
    Biagioli, C., Francesconi, E., Spinosa, P., Taddei, M.: The NIR project. Standards and tools for legislative drafting and legal document Web publication. In: Proc. of the International Conference of Artificial Intelligence and Law. Edinburgh, June 24 (2003)Google Scholar
  3. 3.
    Bolioli, A., Dini, L., Mercatali, P., Romano, F.: For the automated mark-up of Italian legislative texts in XML. In: Proc. of JURIX 2002, London, UK, December 16-17 (2002) Google Scholar
  4. 4.
    De Busser, R., Angheluta, R., Moens, M.-F.: Semantic Case Role Detection for Information Extraction. In: Proc. of COLING 2002, New Brunswick, pp. 1198–1202 (2002)Google Scholar
  5. 5.
    Graubitz, H., Winkler, K., Spiliopoulou, M.: Semantic Tagging of Domain-Specific Text Documents with DIAsDEM. In: Proc. of the 1st International Workshop on Databases, Documents, and Information Fusion (DBFusion 2001), Gommern, Germany, pp. 61–72 (2001)Google Scholar
  6. 6.
    Saias, J., Quaresma, P.: Using NLP techniques to create legal ontologies in a logic programming based web information retrieval system. In: Proc. of ICAIL 2003 Workshop on Legal Ontologies and Web Based Legal Information Management. Edinburgh, UK, June 24-28 (2003)Google Scholar
  7. 7.
    Lame, G.: Using text analysis techniques to identify legal ontologies’ components. In: Proc. of ICAIL 2003 Workshop on Legal Ontologies and Web Based Legal Information Management. Edinburgh, UK, June 24-28 (2003) Google Scholar
  8. 8.
    Mommers, L.: A knowledge-based ontology for the legal domain. In: Proc. of the Second International Workshop on Legal Ontologies, December 13 (2001) Google Scholar
  9. 9.
    Bartolini, R., Lenci, A., Montemagni, S., Pirrelli, V.: Grammar and lexicon in the robust parsing of Italian: Towards a non-naïve interplay. In: Proc. of Coling 2002 Workshop on Grammar Engineering and Evaluation. Academia Sinica, Nankang, Taipei, Taiwan, September 1 (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Roberto Bartolini
    • 1
  • Alessandro Lenci
    • 2
  • Simonetta Montemagni
    • 1
  • Vito Pirrelli
    • 1
  • Claudia Soria
    • 1
  1. 1.Istituto di Linguistica Computazionale CNRPisaItaly
  2. 2.Dipartimento di LinguisticaUniversità di PisaPisaItaly

Personalised recommendations