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Using Legal Ontologies with Rules for Legal Textual Entailment

  • Biralatei Fawei
  • Adam WynerEmail author
  • Jeff Z. Pan
  • Martin Kollingbaum
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10791)

Abstract

Law is an explicit system of rules to govern the behaviour of people. Legal practitioners must learn to apply legal knowledge to the facts at hand. The United States Multistate Bar Exam (MBE) is a professional test of legal knowledge, where passing indicates that the examinee understands how to apply the law. This paper describes an initial attempt to model and implement the automatic application of legal knowledge using a rule-based approach. An NLP tool extracts information (e.g. named entities and syntactic triples) to instantiate an ontology relative to concepts and relations; ontological elements are associated with legal rules written in SWRL to draw inferences to an exam question. The preliminary results on a small sample are promising. However, the main development is the methodology and identification of key issues for future analysis.

References

  1. 1.
    Kim, M.Y., Xu, Y., Lu, Y., Goebel, R.: Legal question answering using paraphrasing and entailment analysis. In: Tenth International Workshop on Juris-Informatics (JURISIN) (2016)Google Scholar
  2. 2.
    Kim, M., Goebel, R.: Two-step cascaded textual entailment for legal bar exam question answering. In: Proceedings of the International Conference on Artificial Intelligence and Law (2017)Google Scholar
  3. 3.
    Manning, C.D., Surdeanu, M., Bauer, J., Finkel, J.R., Bethard, S., McClosky, D.: The Stanford CoreNLP natural language processing toolkit. In: ACL (System Demonstrations), pp 55–60 (2014)Google Scholar
  4. 4.
    Ferrucci, D., Levas, A., Bagchi, S., Gondek, D., Mueller, E.T.: Watson: beyond jeopardy!. Artif. Intell. 199–200, 93–105 (2013)CrossRefGoogle Scholar
  5. 5.
    Monroy, A., Calvo, H., Gelbukh, A.: NLP for shallow question answering of legal documents using graphs. In: Gelbukh, A. (ed.) CICLing 2009. LNCS, vol. 5449, pp. 498–508. Springer, Heidelberg (2009).  https://doi.org/10.1007/978-3-642-00382-0_40CrossRefGoogle Scholar
  6. 6.
    Arya, A., Yaligar, V., Prabhu, R.D., Reddy, R., Acharaya, R.: A knowledge based approach for recognizing textual entailment for natural language inference using data mining. Int. J. Comput. Sci. Eng. 2(06), 2133–2140 (2010)Google Scholar
  7. 7.
    Marsi, E., Krahmer, E., Bosma, W.: Dependency-based paraphrasing for recognizing textual entailment. In: Proceedings of the ACL-PASCAL WS on Textual Entailment and Paraphrasing, pp. 83–88 (2007)Google Scholar
  8. 8.
    Zanzotto, F.M., Moschitti, A., Pennacchiotti, M., Pazienza, M.T.: Learning textual entailment from examples. In: Proceedings of the Second PASCAL Challenges Workshop on Recognising Textual Entailment, vol. 6, no. 09, pp. 50–55 (2006)Google Scholar
  9. 9.
    Do, P.K., Nguyen, H.T., Tran, C.X., Nguyen, M.T., Nguyen, M.L.: Legal question answering using ranking SVM and deep convolutional neural network. In: Tenth International Workshop on Juris-Informatics (JURISIN) (2017)Google Scholar
  10. 10.
    Fawei, B., Wyner, A.Z., Pan, J.Z.: Passing a USA national bar exam: a first corpus for experimentation. In: Tenth International Conference on Language Resources and Evaluation, LREC 2016 (2016)Google Scholar
  11. 11.
    Emmanuel, S.L.: Strategies and Tactics for the MBE (Multistate Bar Exam). Wolters Kluwer, New York (2011)Google Scholar
  12. 12.
    Liu, P., Qiu, X., Huang, X.: Modelling interaction of sentence pair with coupled-LSTMs. arXiv preprint arXiv:1605.09090 (2016)
  13. 13.
    Pan, J.Z.: A flexible ontology reasoning architecture for the semantic web. IEEE Trans. Knowl. Data Eng. 19(2), 246–260 (2007)CrossRefGoogle Scholar
  14. 14.
    Chen, Q., Zhu, X., Ling, Z.H., Wei, S., Jiang, H., Inkpen, D.: Enhanced LSTM for natural language inference. In: Proceedings of the Second Workshop on Evaluating Vector Space Representations for NLP (RepEval 2017) (2017)Google Scholar
  15. 15.
    Kolawole John, A., Di Caro, L., Robaldo, L., Boella, G.: Textual inference with tree-structured LSTM. In: Bosse, T., Bredeweg, B. (eds.) BNAIC 2016. CCIS, vol. 765, pp. 17–31. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-67468-1_2CrossRefGoogle Scholar
  16. 16.
    Wu, Z., Palmer, M.: Verbs semantics and lexical selection. In: Proceedings of the 32nd annual meeting on Association for Computational Linguistics, pp. 133–138 (1994)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Biralatei Fawei
    • 1
  • Adam Wyner
    • 1
    Email author
  • Jeff Z. Pan
    • 1
  • Martin Kollingbaum
    • 1
  1. 1.Department of Computing ScienceUniversity of AberdeenAberdeenUK

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