Combining Natural Language Processing Approaches for Rule Extraction from Legal Documents

  • Mauro DragoniEmail author
  • Serena Villata
  • Williams Rizzi
  • Guido Governatori
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10791)


Legal texts express conditions in natural language describing what is permitted, forbidden or mandatory in the context they regulate. Despite the numerous approaches tackling the problem of moving from a natural language legal text to the respective set of machine-readable conditions, results are still unsatisfiable and it remains a major open challenge. In this paper, we propose a preliminary approach which combines different Natural Language Processing techniques towards the extraction of rules from legal documents. More precisely, we combine the linguistic information provided by WordNet together with a syntax-based extraction of rules from legal texts, and a logic-based extraction of dependencies between chunks of such texts. Such a combined approach leads to a powerful solution towards the extraction of machine-readable rules from legal documents. We evaluate the proposed approach over the Australian “Telecommunications consumer protections code”.


  1. 1.
    van Engers, T., van Gog, R., Sayah, K.: A case study on automated norm extraction. In: Proceedings of JURIX, pp. 49–58 (2004)Google Scholar
  2. 2.
    Wyner, A., Peters, W.: On rule extraction from regulations. In: JURIX 2011, pp. 113–122 (2011)Google Scholar
  3. 3.
    Curran, J.R., Clark, S., Bos, J.: Linguistically motivated large-scale NLP with c&c and boxer. In: Carroll, J.A., van den Bosch, A., Zaenen, A. (eds.) ACL 2007, Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics, 23–30 June 2007, Prague, Czech Republic. The Association for Computational Linguistics (2007)Google Scholar
  4. 4.
    Soria, C., Bartolini, R., Lenci, A., Montemagni, S., Pirrelli, V.: Automatic extraction of semantics in law documents. European Press Academic Publishing (2005)Google Scholar
  5. 5.
    Biagioli, C., Francesconi, E., Passerini, A., Montemagni, S., Soria, C.: Automatic semantics extraction in law documents. In: ICAIL 2015, pp. 133–140 (2005)Google Scholar
  6. 6.
    de Araujo, D.A., Rigo, S., Muller, C., de Oliveira Chishman, R.L.: Automatic information extraction from texts with inference and linguistic knowledge acquisition rules. In: Web Intelligence/IAT Workshops, pp. 151–154 (2013)Google Scholar
  7. 7.
    Kiyavitskaya, N., et al.: Automating the extraction of rights and obligations for regulatory compliance. In: Li, Q., Spaccapietra, S., Yu, E., Olivé, A. (eds.) ER 2008. LNCS, vol. 5231, pp. 154–168. Springer, Heidelberg (2008). Scholar
  8. 8.
    de Maat, E., Winkels, R.: Suggesting model fragments for sentences in Dutch laws. In: Proceedings of LOAIT, pp. 19–28 (2010)Google Scholar
  9. 9.
    Brighi, R., Palmirani, M.: Legal text analysis of the modification provisions: a pattern oriented approach. In: ICAIL 2009, pp. 238–239 (2009)Google Scholar
  10. 10.
    Francesconi, E.: Legal rules learning based on a semantic model for legislation. In: Proceedings of SPLeT Workshop (2010)Google Scholar
  11. 11.
    Boella, G., Di Caro, L., Robaldo, L.: Semantic relation extraction from legislative text using generalized syntactic dependencies and support vector machines. In: Morgenstern, L., Stefaneas, P., Lévy, F., Wyner, A., Paschke, A. (eds.) RuleML 2013. LNCS, vol. 8035, pp. 218–225. Springer, Heidelberg (2013). Scholar
  12. 12.
    Dragoni, M., Governatori, G., Villata, S.: Automated rules generation from natural language legal texts. In: ICAIL 2015 Workshop on Automated Detection, Extraction and Analysis of Semantic Information in Legal Texts (2015)Google Scholar
  13. 13.
    Fellbaum, C.: WordNet: An Electronic Lexical Database. MIT Press, Cambridge (1998)zbMATHGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Mauro Dragoni
    • 1
    Email author
  • Serena Villata
    • 2
  • Williams Rizzi
    • 3
  • Guido Governatori
    • 4
  1. 1.Fondazione Bruno KesslerTrentoItaly
  2. 2.CNRS, I3S LaboratoryParisFrance
  3. 3.Universitá degli Studi di TrentoTrentoItaly
  4. 4.NICTA QueenslandBrisbaneAustralia

Personalised recommendations