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JSAI International Symposium on Artificial Intelligence

JSAI-isAI 2014: New Frontiers in Artificial Intelligence pp 259–273Cite as

Translating Simple Legal Text to Formal Representations

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9067))

Abstract

Various logical representations and frameworks have been proposed for reasoning with legal information. These approaches assume that the legal text has already been translated to the desired formal representation. However, the approaches for translating legal text into formal representations have mostly focused on inferring facts from text or translating it to a single representation. In this work, we use the NL2KR system to translate legal text into a wide variety of formal representations. This will enable the use of existing logical reasoning approaches on legal text (English), thus allowing reasoning with text.

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Notes

  1. 1.

    NL2KR cannot be said to be based on Montague Semantics as it does not use intensional semantics. The translation of natural language to formal language with the use of lambda calculus, however, is in the same spirit as Montague’s approach.

  2. 2.

    # is used in place of \(\lambda \) to enable typing into a terminal.

  3. 3.

    The choice of intermediate language depends on the domain and target languages. Once an intermediate language has been decided, the conversion can be automated.

  4. 4.

    Let the required function be A and the required argument be B. Let the CCG-determined function be B and the CCG-determined argument be A. Recall that @ denotes \(\lambda \) application. By giving a meaning of the form \(\#x.(x@b)\) to B, and performing application as determined by CCG, we obtain the result as \((\#x.(x@b))@a\) or a@b.

References

  1. Jurisin legal information extraction and entailment competition (2014). http://webdocs.cs.ualberta.ca/miyoung2/jurisin_task/index.html

  2. Aït-Mokhtar, S., Chanod, J.P., Roux, C.: Robustness beyond shallowness: incremental deep parsing. Nat. Lang. Eng. 8(3), 121–144 (2002)

    Article  Google Scholar 

  3. Androutsopoulos, I., Malakasiotis, P.: A survey of paraphrasing and textual entailment methods. J. Artif. Int. Res. 38(1), 135–187 (2010)

    MATH  Google Scholar 

  4. Bajwa, I.B., Behzad, L.M.: SBVR business rules generation from natural language specification. In: AAAI 2011 Spring Symposium AI for Business Agility, San Francisco, USA, pp. 2–8 (2011)

    Google Scholar 

  5. Baral, C., Dzifcak, J., Gonzalez, M.A., Zhou, J.: Using Inverse lambda and Generalization to Translate English to Formal Languages. CoRR abs/1108.3843 (2011)

    Google Scholar 

  6. Blackburn, P., Bos, J.: Representation and Inference for Natural Language: A First Course in Computational Semantics. Center for the Study of Language and Information, Stanford (2005)

    Google Scholar 

  7. Bos, J., Markert, K.: Recognising textual entailment with logical inference. In: Proceedings of the Conference on Human Language Technology and Empirical Methods in Natural Language Processing, HLT 2005, pp. 628–635. Association for Computational Linguistics, Stroudsburg (2005)

    Google Scholar 

  8. Brüninghaus, S., Ashley, K.D.: Improving the representation of legal case texts with information extraction methods. In: Proceedings of the 8th International Conference on Artificial Intelligence and Law, ICAIL 2001, pp. 42–51. ACM, New York (2001)

    Google Scholar 

  9. Church, A.: An unsolvable problem of elementary number theory. Am. J. Math. 58(2), 345–363 (1936)

    Article  MathSciNet  Google Scholar 

  10. Costantini, S., Paolucci, A.: Towards translating natural language sentences into ASP. In: Faber, W., Leone, N. (eds.) CILC, CEUR Workshop Proceedings, vol. 598. CEUR-WS.org (2010)

    Google Scholar 

  11. De Vos, M., Padget, J., Satoh, K.: Legal modelling and reasoning using institutions. In: Bekki, D. (ed.) JSAI-isAI 2010. LNCS, vol. 6797, pp. 129–140. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  12. Distinto, I., Guarino, N., Masolo, C.: A well-founded ontological framework for modeling personal income tax. In: Proceedings of the Fourteenth International Conference on Artificial Intelligence and Law, ICAIL 2013, pp. 33–42. ACM, New York (2013)

    Google Scholar 

  13. Giblin, C., Liu, A.Y., Müller, S., Pfitzmann, B., Zhou, X.: Regulations expressed as logical models (REALM). In: Proceedings of the 2005 Conference on Legal Knowledge and Information Systems, JURIX 2005, The Eighteenth Annual Conference, pp. 37–48. IOS Press, Amsterdam (2005)

    Google Scholar 

  14. Giordano, L., Martelli, A., Dupré, D.T.: Temporal deontic action logic for the verification of compliance to norms in ASP. In: Proceedings of the Fourteenth International Conference on Artificial Intelligence and Law, ICAIL 2013, pp. 53–62. ACM, New York (2013)

    Google Scholar 

  15. Hoekstra, R., Breuker, J., Bello, M.D., Boer, E.: The LKIF core ontology of basic legal concepts. In: Proceedings of the Workshop on Legal Ontologies and Artificial Intelligence Techniques, LOAIT 2007 (2007)

    Google Scholar 

  16. 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, vol. 5447, pp. 242–253. Springer, Heidelberg (2009)

    Chapter  Google Scholar 

  17. Lagos, N., Segond, F., Castellani, S., O’Neill, J.: Event extraction for legal case building and reasoning. In: Shi, Z., Vadera, S., Aamodt, A., Leake, D. (eds.) IIP 2010. IFIP AICT, vol. 340, pp. 92–101. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  18. McCarty, L.T.: Deep semantic interpretations of legal texts. In: Proceedings of the 11th International Conference on Artificial Intelligence and Law, ICAIL 2007, pp. 217–224. ACM, New York (2007)

    Google Scholar 

  19. Montague, R.: English as a formal language. In: Thomason, R.H. (ed.) Formal Philosophy: Selected Papers of Richard Montague, pp. 188–222. Yale University Press, New Haven (1974)

    Google Scholar 

  20. 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)

    Chapter  Google Scholar 

  21. Riveret, R., Rotolo, A., Contissa, G., Sartor, G., Vasconcelos, W.: Temporal accommodation of legal argumentation. In: Proceedings of the 13th International Conference on Artificial Intelligence and Law, ICAIL 2011, pp. 71–80. ACM, New York (2011)

    Google Scholar 

  22. Steedman, M.: The Syntactic Process. MIT Press, Cambridge (2000)

    MATH  Google Scholar 

  23. Zettlemoyer, L.S., Collins, M.: Learning to map sentences to logical form: structured classification with probabilistic categorial grammars. In: UAI, pp. 658–666. AUAI Press (2005)

    Google Scholar 

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Acknowledgements

We thank Arindam Mitra and Somak Aditya for their work in developing the L-Parser. We thank NSF for the DataNet Federation Consortium grant OCI-0940841 and ONR for their grant N00014-13-1-0334 for partially supporting the development of NL2KR.

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Authors and Affiliations

  1. Arizona State University, Tempe, AZ, USA

    Shruti Gaur, Nguyen H. Vo, Kazuaki Kashihara & Chitta Baral

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  1. Shruti Gaur
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  2. Nguyen H. Vo
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  3. Kazuaki Kashihara
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  4. Chitta Baral
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Correspondence to Shruti Gaur .

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Editors and Affiliations

  1. Tokyo Institute of Technology, Tokyo, Japan

    Tsuyoshi Murata

  2. Ochanomizu University, Tokyo, Japan

    Koji Mineshima

  3. Department Information Science, Faculty of Science, Ochanomizu University , Tokyo, Japan

    Daisuke Bekki

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Gaur, S., Vo, N.H., Kashihara, K., Baral, C. (2015). Translating Simple Legal Text to Formal Representations. In: Murata, T., Mineshima, K., Bekki, D. (eds) New Frontiers in Artificial Intelligence. JSAI-isAI 2014. Lecture Notes in Computer Science(), vol 9067. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-48119-6_19

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  • DOI: https://doi.org/10.1007/978-3-662-48119-6_19

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-48118-9

  • Online ISBN: 978-3-662-48119-6

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