Advertisement

A Machine Learning Approach to Argument Mining in Legal Documents

  • Prakash PoudyalEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10791)

Abstract

This study aims to analyze and evaluate the natural language arguments present in legal documents. The research is divided into three modules or stages: an Argument Element Identifier Module identifying argumentative and non-argumentative sentences in legal texts; an Argument Builder Module handling clustering of argument’s components; and an Argument Structurer Module distinguishing argument’s components (premises and conclusion). The corpus selected for this research was the set of Case-Laws issued by the European Court of Human Rights (ECHR) annotated by Mochales-Palau and Moens [8]. The preliminary results of the Argument Element Identifier Module are presented, including its main features. The performance of two machine learning algorithms (Support Vector Machine Algorithm and Random Forest Algorithm) is also measured.

Keywords

Legal argument Natural language analysis Machine learning 

Notes

Acknowledgment

The current work is funded by EMMA-WEST in the framework of the EU Erasmus Mundus Action 2.

References

  1. 1.
    Biran, O., Rambow, O.: Identifying justifications in written dialogs by classifying text as argumentative. Int. J. Semant. Comput. 5(04), 363–381 (2011).  https://doi.org/10.1142/S1793351X11001328CrossRefzbMATHGoogle Scholar
  2. 2.
    Bunescu, R.C., Mooney, R.J.: A shortest path dependency kernel for relation extraction. In: Proceedings of the Human Language Technology Conference and Conference Empirical methods in Natural Language Processing (HLT/EMNLP-05), pp. 724–731. Association for Computational Linguistics, Stroudsburg (2005).  https://doi.org/10.3115/1220575.1220666
  3. 3.
    Cabrio, E., Villata, S.: Towards a benchmark of natural language arguments. In: Proceedings of the 15th International Workshop on Non-Monotonic Reasoning (NMR 2014), Vienna (2014)Google Scholar
  4. 4.
    Doddington, G., Mitchell, A., Przybocki, M., Ramshaw, L., Strassel, S., Weischedel, R.: The automatic content extraction (ace) program-tasks, data, and evaluation. In: Proceedings of the Fourth International Conference on Language Resources and Evaluation, vol. 2, pp. 837–840 (2004)Google Scholar
  5. 5.
    Florou, E., Konstantopoulos, S., Koukourikos, A., Karampiperis, P.: Argument extraction for supporting public policy formulation. In: Proceedings of the 7th Workshop on Language Technology for Cultural Heritage, Social Sciences, and Humanities, pp. 49–54 (2013)Google Scholar
  6. 6.
    Mochales, R., Ieven, A.: Creating an argumentation corpus: do theories apply to real arguments?: a case study on the legal argumentation of the ECHR. In: Proceedings of the 12th International Conference on Artificial Intelligence and Law, pp. 21–30. ACM, New York (2009).  https://doi.org/10.1145/1568234.1568238
  7. 7.
    Mochales, R., Moens, M.F.: Study on the structure of argumentation in case law. In: Proceedings of the 2008 Conference on Legal Knowledge and Information Systems, pp. 11–20. IOS Press, Amsterdam (2008)Google Scholar
  8. 8.
    Mochales-Palau, R., Moens, M.F.: Study on sentence relations in the automatic detection of argumentation in legal cases. Front. Artif. Intell. Appl. 165, 89–98 (2007)Google Scholar
  9. 9.
    Moens, M.F., Boiy, E., Palau, R.M., Reed, C.: Automatic detection of arguments in legal texts. In: Proceedings of the 11th International Conference on Artificial Intelligence and Law, pp. 225–230. ACM (2007)Google Scholar
  10. 10.
    Palau, R.M., Moens, M.F.: Argumentation mining: the detection, classification and structure of arguments in text. In: Proceedings of the 12th International Conference on Artificial Intelligence and Law, pp. 98–107. ACM (2009).  https://doi.org/10.1145/1568234.1568246
  11. 11.
    Poudyal, P., Goncalves, T., Quaresma, P.: Experiments on identification of argumentative sentences. In: Proceeding of 10th International Conference on Software, Knowledge, Information Management & Applications (SKIMA), pp. 398–403. IEEE (2016).  https://doi.org/10.1109/SKIMA.2016.7916254
  12. 12.
    Poudyal, P., Quaresma, P.: An hybrid approach for legal information extraction. Front. Artif. Intell. Appl. (JURIX) 250, 115–118 (2012).  https://doi.org/10.3233/978-1-61499-167-0-115CrossRefGoogle Scholar
  13. 13.
    Reed, C., Palau, R.M., Rowe, G., Moens, M.F.: Language resources for studying argument. In: Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC 2008), Marrakech, Morocco, pp. 91–100 (2008)Google Scholar
  14. 14.
    Salton, G., Wong, A., Yang, C.S.: A vector space model for automatic indexing. Commun. ACM 18(11), 613–620 (1975).  https://doi.org/10.1145/361219.361220CrossRefzbMATHGoogle Scholar
  15. 15.
    Stab, C., Gurevych, I.: Identifying argumentative discourse structures in persuasive essays. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 46–56 (2014).  https://doi.org/10.3115/v1/D14-1006
  16. 16.
    Stab, C., Kirschner, C., Eckle-Kohler, J., Gurevych, I.: Argumentation mining in persuasive essays and scientific articles from the discourse structure perspective. In: Proceedings with the Workshop on Frontiers and Connections between Argumentation Theory and Natural Language Processing, Bertinoro, Italy, pp. 40–49 (2014)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Department of InformaticsUniversity of ÉvoraÉvoraPortugal

Personalised recommendations