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JSAI International Symposium on Artificial Intelligence

JSAI-isAI 2019: New Frontiers in Artificial Intelligence pp 64–81Cite as

Automatic Extraction of Legal Norms: Evaluation of Natural Language Processing Tools

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12331))

Abstract

Extracting and formalising legal norms from legal documents is a time-consuming and complex procedure. Therefore, the automatic methods that can accelerate this process are in high demand. In this paper, we address two major questions related to this problem: (i) what are the challenges in formalising legal documents into a machine understandable formalism? (ii) to what extent can the data-driven state-of-the-art approaches developed in the Natural Language Processing (NLP) community be used to automate the normative mining process. The results of our experiments indicate that NLP technologies such as relation extraction and semantic parsing are promising research avenues to advance research in this area.

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Notes

  1. 1.

    Here, the term generation is used in a loose way (normative rules can be generated or extracted or distilled or built, etc.), and is not used to refer exclusively to the text generation problem study by the Natural Language Generation community.

  2. 2.

    Available at: http://bitbucket.csiro.au/users/fer201/repos/regtech-dataset.

References

  1. Angeli, G., Johnson Premkumar, M.J., Manning, C.D.: Leveraging linguistic structure for open domain information extraction. In: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics, ACL 2015, Beijing, China, pp. 344–354 (2015)

    Google Scholar 

  2. Banarescu, L., et al.: Abstract meaning representation for sembanking. In: Pareja-Lora, A., Liakata, M., Dipper, S. (eds.) Proceedings of the 7th Linguistic Annotation Workshop and Interoperability with Discourse, Sofia, Bulgaria, pp. 178–186, August 2013

    Google Scholar 

  3. 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). https://doi.org/10.1007/978-3-642-39617-5_20

    Chapter  Google Scholar 

  4. Breaux, T.D., Antón, A.I.: A systematic method for acquiring regulatory requirements: a frame-based approach. In: Proceedings of the 6th International Workshop on Requirements for High Assurance Systems, Pittsburg, PA, USA, September 2007

    Google Scholar 

  5. Burga, A., Codina, J., Ferraro, G., Saggion, H., Wanner, L.: The challenge of syntactic dependency parsing adaptation for the patent domain. In: Proceedings of ESSLLI-13 Workshop on Extrinsic Parse Improvement. EPI, Düsseldorf, Germany, August 2013

    Google Scholar 

  6. Dong, L., Lapata, M.: Language to logical form with neural attention. In: Erk, K., Smith, N.A. (eds.) Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, vol. 1: Long Papers, Berlin, German, pp. 33–43, August 2016

    Google Scholar 

  7. Dragoni, M., Villata, S., Rizzi, W., Governatori, G.: Combining natural language processing approaches for rule extraction from legal documents. In: Pagallo, U., Palmirani, M., Casanovas, P., Sartor, G., Villata, S. (eds.) AICOL 2015-2017. LNCS (LNAI), vol. 10791, pp. 287–300. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00178-0_19

    Chapter  Google Scholar 

  8. Dragoni, M., Villata, S., Rizzi, W., Governatori, G.: Combining NLP approaches for rule extraction from legal documents. In: 1st Workshop on MIning and REasoning with Legal texts (MIREL 2016), Sophia Antipolis, France, December 2016

    Google Scholar 

  9. van Engers, T.M., van Gog, R., Sayah, K.: A case study on automated norm extraction. In: Gordon, T. (ed.) The 17th International Conference on Legal Knowledge and Information Systems, JURIX 2004, pp. 49–58. IOS Press, Amsterdam (2004)

    Google Scholar 

  10. Fan, X., Monti, E., Mathias, L., Dreyer, M.: Transfer learning for neural semantic parsing. In: Blunsom, P., et al. (eds.) Proceedings of the 2nd Workshop on Representation Learning for NLP, Rep4NLP 2017, Vancouver, Canada, pp. 48–56, August 2017

    Google Scholar 

  11. Finegan-Dollak, C., et al.: Improving text-to-SQL evaluation methodology. In: Gurevych, I., Miyao, Y. (eds.) Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, ACL 2018, Melbourne, Australia, pp. 351–360, July 2018

    Google Scholar 

  12. Gaur, S., Vo, N.H., Kashihara, K., Baral, C.: Translating simple legal text to formal representations. In: Murata, T., Mineshima, K., Bekki, D. (eds.) JSAI-isAI 2014. LNCS (LNAI), vol. 9067, pp. 259–273. Springer, Heidelberg (2015). https://doi.org/10.1007/978-3-662-48119-6_19

    Chapter  Google Scholar 

  13. Gordon, A.S., Hobbs, J.R.: A Formal Theory of Commonsense Psychology: How People Think People Think, 1st edn. Cambridge University Press, New York (2017)

    Book  Google Scholar 

  14. Governatori, G., Rotolo, A.: A conceptually rich model of business process compliance. In: Proceedings of the 7th Asia-Pacific Conference on Conceptual Modelling, APCCM 2010, ACS, Brisbane, QLD, Australia, pp. 3–12, January 2010

    Google Scholar 

  15. Herzig, J., Berant, J.: Neural semantic parsing over multiple knowledge-bases. In: Barzilay, R., Kan, M.Y. (eds.) Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, ACL 2017, Vancouver, Canada, pp. 623–628, July 2017

    Google Scholar 

  16. Jia, R., Liang, P.: Data recombination for neural semantic parsing. In: Erka, K., Smith, N.A. (eds.) Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, ACL 2016, Berlin, Germany, pp. 12–22, August 2016

    Google Scholar 

  17. Kelsen, H.: General Theory of Norms. Oxford University Press, Inc., Oxford (1991)

    Book  Google Scholar 

  18. Kennardi, A., Ferraro, G., Wang, Q.: Domain adaptation for low-resource neural semantic parsing. In: MacKinlay, A., Piccardi, M. (eds.) Proceedings of the 17th Workshop of the Australasian Language Technology Association, ALTA 2019, Sydney, Australia, pp. 107–113 (2019)

    Google Scholar 

  19. Kimura, Y., Nakamura, M., Shimazu, A.: Treatment of legal sentences including itemized and referential expressions – towards translation into logical forms. In: Hattori, H., Kawamura, T., Idé, T., Yokoo, M., Murakami, Y. (eds.) JSAI 2008. LNCS (LNAI), vol. 5447, pp. 242–253. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-00609-8_21

    Chapter  Google Scholar 

  20. Kočiský, T., et al.: Semantic parsing with semi-supervised sequential autoencoders. In: Su, J., Duh, K., Carreras, X. (eds.) Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, EMNLP 2016, pp. 1078–1087. Association for Computational Linguistics, Austin, November 2016

    Google Scholar 

  21. Kwiatkowski, T., Zettlemoyer, L., Goldwater, S., Steedman, M.: Lexical generalization in CCG grammar induction for semantic parsing. In: Barzilay, R., Johnsn, M. (eds.) Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing, EMNLP 2011, Edinburgh, Scotland, UK, pp. 1512–1523, July 2011

    Google Scholar 

  22. Lapata, M., Dong, L.: Coarse-to-fine decoding for neural semantic parsing. In: Gurevych, I., Miyao, Y. (eds.) Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, ACL 2018, Melbourne, Australia, pp. 731–742, July 2018

    Google Scholar 

  23. Makinson, D.: On a fundamental problem of deontic logic. In: McNamara, P., Prakken, H. (eds.) Norms, Logics and Information Systems. New Studies in Deontic Logic and Computer Science, vol. 49, pp. 29–53. IOS Press, Amsterdam (1999)

    Google Scholar 

  24. Miall, H.: Emergent Conflict and Peaceful Change. Palgrave MacMillan, London (2007)

    Book  Google Scholar 

  25. Ministry of Business, Innovation and Employment, New Zealand: New Zealand Building Code Clause G4 Ventilation: Acceptable Solutions and Verification Methods, 4th edn. (2017)

    Google Scholar 

  26. Nakamura, M., Nobuoka, S., Shimazu, A.: Towards translation of legal sentences into logical forms. In: Satoh, K., Inokuchi, A., Nagao, K., Kawamura, T. (eds.) JSAI 2007. LNCS (LNAI), vol. 4914, pp. 349–362. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-78197-4_33

    Chapter  Google Scholar 

  27. Niklaus, C., Cetto, M., Freitas, A., Handschuh, S.: Transforming complex sentences into a semantic hierarchy. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, Florence, Italy, pp. 3415–3427, July 2019

    Google Scholar 

  28. OASIS LegalRuleML TC: OASIS LegalRuleML (2013). http://www.oasis-open.org/committees/legalruleml. Accessed 7 Dec 2019

  29. Popple, J.: A Pragmatic Legal Expert System. Dartmouth (1996)

    Google Scholar 

  30. Rubino, R., Rotolo, A., Sartor, G.: An OWL ontology of fundamental legal concepts. In: Proceedings of the 9th Annual Conference on Legal Knowledge and Information Systems, JURIX 2006, pp. 101–110. IOS Press, Amsterdam (2006)

    Google Scholar 

  31. Sartor, G.: Legal Reasoning: A Cognitive Approach to the Law, A Treatise of Legal Philosophy and General Jurisprudence, vol. 5. Springer, Berlin (2005)

    Google Scholar 

  32. Schwarm, S.E., Ostendorf, M.: Reading level assessment using support vector machines and statistical language models. In: Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics, ACL 2005, Stroudsburg, PA, USA, pp. 523–530 (2005)

    Google Scholar 

  33. Sleimi, A., Sannier, N., Sabetzadeh, M., Briand, L., Dann, J.: Automated extraction of semantic legal metadata using natural language processing. In: The 26th IEEE International Requirements Engineering Conference, Banff, AB, Canada, pp. 124–135. IEEE, August 2018

    Google Scholar 

  34. Susanto, R.H., Lu, W.: Neural architectures for multilingual semantic parsing. In: Barzilay, R., Kan, M.Y. (eds.) Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, ACL 2017, Vancouver, Canada, pp. 38–44, July 2017

    Google Scholar 

  35. Tjong, E.F., Sang, K., Déjean, H.: Introduction to the CoNLL-2001 shared task: clause identification. In: Daelemans, W., Zajac, R. (eds.) Proceedings of the 2001 Workshop on Computational Natural Language Learning, ConLL 2001. Association for Computational Linguistics, Toulouse, July 2001

    Google Scholar 

  36. Vajjala, S., Meurers, D.: Readability-based Sentence Ranking for Evaluating Text Simplification. CoRR (2016). http://arxiv.org/abs/1603.06009

  37. Wieringa, R.J., Meyer, J. J. C.: Applications of deontic logic in computer science: a concise overview. In: Meyer, J.J.C., Wieringa, R.J. (eds.) International Workshop on Deontic Logic in Computer Science, DEON 1993, Amsterdam, Netherlands, pp. 17–40, December 1993

    Google Scholar 

  38. Woodsend, K., Lapata, M.: WikiSimple: automatic simplification of wikipedia articles. In: Proceedings of the 25th AAAI Conference on Artificial Intelligence, AAAI 2011, San Francisco, CA, USA, pp. 927–932. AAAI Press, August 2011

    Google Scholar 

  39. Wyner, A.Z., Peters, W.: On rule extraction from regulations. In: Atkinson, K.M. (ed.) Proceedings of the 24th International Conference on Legal Knowledge and Information, JURIX 2011, Vienna, Austria, pp. 113–122. IOS Press, September 2011

    Google Scholar 

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Author information

Authors and Affiliations

  1. Data61, CSIRO, Sydney, Australia

    Gabriela Ferraro, Ho-Pun Lam, Silvano Colombo Tosatto, Francesco Olivieri, Mohammad Badiul Islam, Nick van Beest & Guido Governatori

  2. Australian National University, Canberra, Australia

    Gabriela Ferraro

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  1. Gabriela Ferraro
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  2. Ho-Pun Lam
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  4. Francesco Olivieri
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  5. Mohammad Badiul Islam
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  6. Nick van Beest
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  7. Guido Governatori
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Correspondence to Gabriela Ferraro .

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Editors and Affiliations

  1. The University of Electro-Communications, Tokyo, Japan

    Maki Sakamoto

  2. Tokyo Institute of Technology, Tokyo, Japan

    Naoaki Okazaki

  3. Keio University, Tokyo, Japan

    Koji Mineshima

  4. National Institute of Informatics, Tokyo, Japan

    Ken Satoh

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Ferraro, G. et al. (2020). Automatic Extraction of Legal Norms: Evaluation of Natural Language Processing Tools. In: Sakamoto, M., Okazaki, N., Mineshima, K., Satoh, K. (eds) New Frontiers in Artificial Intelligence. JSAI-isAI 2019. Lecture Notes in Computer Science(), vol 12331. Springer, Cham. https://doi.org/10.1007/978-3-030-58790-1_5

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  • DOI: https://doi.org/10.1007/978-3-030-58790-1_5

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-030-58790-1

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