Lexical-Morphological Modeling for Legal Text Analysis

  • Danilo S. CarvalhoEmail author
  • Minh-Tien Nguyen
  • Chien-Xuan Tran
  • Minh-Le Nguyen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10091)


In the context of the Competition on Legal Information Extraction/Entailment (COLIEE), we propose a method comprising the necessary steps for finding relevant documents to a legal question and deciding on textual entailment evidence to provide a correct answer. The proposed method is based on the combination of several lexical and morphological characteristics, to build a language model and a set of features for Machine Learning algorithms. We provide a detailed study on the proposed method performance and failure cases, indicating that it is competitive with state-of-the-art approaches on Legal Information Retrieval and Question Answering, while not needing extensive training data nor depending on expert produced knowledge. The proposed method achieved significant results in the competition, indicating a substantial level of adequacy for the tasks addressed.


Relevant Article Civil Code Legal Text Relevance Analysis Lexical Feature 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This work is supported partly by the grant of NII Research Cooperation and JAIST’s Research grant.


  1. 1.
    Berring, R.C.: The Heart of Legal Information: The Crumbling Infrastructure of Legal Research. Legal Information and the Development of American Law. Thomson/West, St. Paul (2008)Google Scholar
  2. 2.
    Hoekstra, R., Breuker, J., Di Bello, M., Boer, A.: The LKIF core ontology of basic legal concepts. In: Proceedings of the Workshop on Legal Ontologies and Artificial Intelligence Techniques (LOAIT) (2007)Google Scholar
  3. 3.
    Liu, Y.-H., Chen, Y.-L., Ho, W.-L.: Predicting associated statutes for legal problems. Inf. Process. Manag. 51(1), 194–211 (2015)CrossRefGoogle Scholar
  4. 4.
    Ha, Q.-T., Ha, T.-O., Nguyen, T.-D., Thi, T.-L.N.: Refining the judgment threshold to improve recognizing textual entailment using similarity. In: Nguyen, N.-T., Hoang, K., Jȩdrzejowicz, P. (eds.) ICCCI 2012. LNCS (LNAI), vol. 7654, pp. 335–344. Springer, Heidelberg (2012). doi: 10.1007/978-3-642-34707-8_34 CrossRefGoogle Scholar
  5. 5.
    Inkpen, D., Kipp, D., Nastase, V.: Machine learning experiments for textual entailment. In: Proceedings of the Second Challenge Workshop Recognising Textual Entailment, pp. 17–20 (2006)Google Scholar
  6. 6.
    Castillo, J.J.: An approach to recognizing textual entailment and TE search task using SVM. Procesamiento del Lenguaje Natural 44, 139–145 (2010)Google Scholar
  7. 7.
    Nguyen, M.-T., Ha, Q.-T., Nguyen, T.-D., Nguyen, T.-T., Nguyen, L.-M.: Recognizing textual entailment in vietnamese text: an experiment study. In: KSE, pp. 108–113 (2015)Google Scholar
  8. 8.
    Pham, M.Q.N., Nguyen, L.M., Shimazu, A.: Using machine translation for recognizing textual entailment in Vietnamese language. In: RIVF, pp. 1–6 (2012)Google Scholar
  9. 9.
    Giampiccolo, D., Magnini, B., Dagan, I., Dolan, B.: The third PASCAL recognising textual entailment challenge. In: Proceedings of the ACL-PASCAL Workshop on Textual Entailment and Paraphrasing. pp. 1–9. Association for Computational Linguistics (2007)Google Scholar
  10. 10.
    Tran, O.T., Ngo, B.X., Nguyen, M., Shimazu, A.: Answering legal questions by mining reference information. In: Nakano, Y., Satoh, K., Bekki, D. (eds.) JSAI-isAI 2013. LNCS (LNAI), vol. 8417, pp. 214–229. Springer, Cham (2014). doi: 10.1007/978-3-319-10061-6_15 Google Scholar
  11. 11.
    Kim, M.-Y., Xu, Y., Goebel, R., Satoh, K.: Answering yes/no questions in legal bar exams. In: Nakano, Y., Satoh, K., Bekki, D. (eds.) JSAI-isAI 2013. LNCS (LNAI), vol. 8417, pp. 199–213. Springer, Cham (2014). doi: 10.1007/978-3-319-10061-6_14 Google Scholar
  12. 12.
    Bach, N.X., Nguyen, L.M., Shimazu, A.: RRE task: the task of recognition of requisite part and effectuation part in law sentences. J. IJCPOL 23(2), 109–130 (2010)Google Scholar
  13. 13.
    Tran, O.T., Bach, N.X., Nguyen, L.M., Shimazu, A.: Reference resolution in legal texts. In: Proceedings of ICAIL, pp. 101–110 (2013)Google Scholar
  14. 14.
    Dagan, I., Dolan, B., Magnini, B., Roth, D.: Recognizing textual entailment: rational, evaluation and approaches - erratum. Nat. Lang. Eng. 16(1), 105 (2010)CrossRefGoogle Scholar
  15. 15.
    Wang, R.: Intrinsic and extrinsic approaches to recognizing textual entailment, pp. 1-219. Saarland University (2011). ISBN: 978-3-933218-32-2Google Scholar
  16. 16.
    Chelba, C., Mikolov, T., Schuster, M., Ge, Q., Brants, T., Koehn, P., Robinson, T.: One billion word benchmark for measuring progress in statistical language modeling, arXiv preprint arXiv:1312.3005 (2013)
  17. 17.
    Mikolov, T., Sutskever, I., Chen, K., Corrado, G., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Proceedings of NIPS (2013)Google Scholar
  18. 18.
    Freund, Y., Schapire, R.E.: A decision-theoretic generalization of on-line learning and an application to boosting. J. Comput. Syst. Sci. 55(1), 119–139 (1997)MathSciNetCrossRefzbMATHGoogle Scholar
  19. 19.
    Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273–297 (1995)zbMATHGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Danilo S. Carvalho
    • 1
    Email author
  • Minh-Tien Nguyen
    • 1
    • 2
  • Chien-Xuan Tran
    • 1
  • Minh-Le Nguyen
    • 1
  1. 1.School of Information ScienceJapan Advanced Institute of Science and Technology (JAIST)NomiJapan
  2. 2.Hung Yen University of Education and Technology (UTEHY)Hung YenVietnam

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