Advertisement

Identifying Participant Mentions and Resolving Their Coreferences in Legal Court Judgements

  • Ajay Gupta
  • Devendra Verma
  • Sachin Pawar
  • Sangameshwar Patil
  • Swapnil Hingmire
  • Girish K. Palshikar
  • Pushpak Bhattacharyya
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11107)

Abstract

Legal court judgements have multiple participants (e.g. judge, complainant, petitioner, lawyer, etc.). They may be referred to in multiple ways, e.g., the same person may be referred as lawyer, counsel, learned counsel, advocate, as well as his/her proper name. For any analysis of legal texts, it is important to resolve such multiple mentions which are coreferences of the same participant. In this paper, we propose a supervised approach to this challenging task. To avoid human annotation efforts for Legal domain data, we exploit ACE 2005 dataset by mapping its entities to participants in Legal domain. We use basic Transfer Learning paradigm by training classification models on general purpose text (news in ACE 2005 data) and applying them to Legal domain text. We evaluate our approach on a sample annotated test dataset in Legal domain and demonstrate that it outperforms state-of-the-art baselines.

Keywords

Legal text mining Coreference resolution Supervised machine learning 

References

  1. 1.
    Agrawal, S., Joshi, A., Ross, J.C., Bhattacharyya, P., Wabgaonkar, H.M.: Are word embedding and dialogue act class-based features useful for coreference resolution in dialogue? In: Proceedings of PACLING (2017)Google Scholar
  2. 2.
    Al-Kofahi, K., Grom, B., Jackson, P.: Anaphora resolution in the extraction of treatment history language from court opinions by partial parsing. In: Proceedings of 7th ICAIL (1999)Google Scholar
  3. 3.
    Bagga, A., Baldwin, B.: Algorithms for scoring coreference chains. In: The First International Conference on Language Resources and Evaluation Workshop on Linguistics Coreference, Granada , vol. 1, pp. 563–566 (1998)Google Scholar
  4. 4.
    Cardellino, C., Teruel, M., Alemany, L.A., Villata, S.: A low-cost, high-coverage legal named entity recognizer, classifier and linker. In: Proceedings of 16th ICAIL (2017)Google Scholar
  5. 5.
    Cardellino, C., Teruel, M., Alemany, L.A., Villata, S.: Ontology population and alignment for the legal domain: YAGO, Wikipedia and LKIF. In: Proceedings of ISWC (2017)Google Scholar
  6. 6.
    Cheri, J., Bhattacharyya, P.: Coreference resolution to support IE from Indian classical music forums. In: Proceedings of RANLP, pp. 91–96 (2015)Google Scholar
  7. 7.
    Dozier, C., Haschart, R.: Automatic extraction and linking of personal names in legal text. In: Proceedings of Recherche d’Informations Assistee par Ordinateur, RIAO 2000 (2000)Google Scholar
  8. 8.
    Jackson, P., Al-Kofahi, K., Tyrrell, A., Vachher, A.: Information extraction from case law and retrieval of prior cases. Artif. Intell. 150, 239–290 (2003)CrossRefGoogle Scholar
  9. 9.
    Kumar, S., Reddy, P.K., Reddy, V.B., Singh, A.: Similarity analysis of legal judgments. In: Proceedings of the COMPUTE (2011)Google Scholar
  10. 10.
    Lafferty, J.D., McCallum, A., Pereira, F.C.N.: Conditional random fields: probabilistic models for segmenting and labeling sequence data. In: Proceedings of the Eighteenth International Conference on Machine Learning, ICML 2001, pp. 282–289. Morgan Kaufmann Publishers Inc., San Francisco (2001). http://dl.acm.org/citation.cfm?id=645530.655813
  11. 11.
    Luo, X.: On coreference resolution performance metrics. In: Proceedings of HLT-EMNLP, pp. 25–32 (2005)Google Scholar
  12. 12.
    Mochales, R., Moens, M.F.: Argumentation mining. Artif. Intell. Law 19(1), 1–22 (2011)CrossRefGoogle Scholar
  13. 13.
    Ng, V.: Machine learning for entity coreference resolution: a retrospective look at two decades of research. In: Proceedings of the 31st AAAI Conference on Artificial Intelligence, pp. 4877–4884 (2017)Google Scholar
  14. 14.
    Peng, H., Chang, K., Roth, D.: A joint framework for coreference resolution and mention head detection. In: CoNLL 2015, pp. 12–21 (2015)Google Scholar
  15. 15.
    Peng, H., Khashabi, D., Roth, D.: Solving hard coreference problems. In: NAACL HLT 2015, pp. 809–819 (2015)Google Scholar
  16. 16.
    Pradhan, S., Luo, X., Recasens, M., Hovy, E., Ng, V., Strube, M.: Scoring coreference partitions of predicted mentions: a reference implementation. In: Proceedings of ACL (2014)Google Scholar
  17. 17.
    Saravanan, M., Ravindran, B., Raman, S.: Improving legal information retrieval using an ontological framework. Artif. Intell. Law 17(2), 101–124 (2011)CrossRefGoogle Scholar
  18. 18.
    Shulayeva, O., Siddharthan, A., Wyner, A.: Recognizing cited facts and principles in legal judgements. Artif. Intell. Law 25(1), 107–126 (2017)CrossRefGoogle Scholar
  19. 19.
    Soon, W.M., Ng, H.T., Lim, D.C.Y.: A machine learning approach to coreference resolution of noun phrases. Comput. Linguist. 27(4), 521–544 (2001)CrossRefGoogle Scholar
  20. 20.
    Venturi, G.: Legal language and legal knowledge management applications. In: Francesconi, E., Montemagni, S., Peters, W., Tiscornia, D. (eds.) Semantic Processing of Legal Texts. LNCS (LNAI), vol. 6036, pp. 3–26. Springer, Heidelberg (2010).  https://doi.org/10.1007/978-3-642-12837-0_1CrossRefGoogle Scholar
  21. 21.
    Vilain, M., Burger, J., Aberdeen, J., Connolly, D., Hirschman, L.: A model-theoretic coreference scoring scheme. In: Proceedings of the 6th Conference on Message Understanding, pp. 45–52 (1995)Google Scholar
  22. 22.
    Walker, C., Strassel, S., Medero, J., Maeda, K.: ACE 2005 multilingual training corpus. Linguist. Data Consortium 57 (2006)Google Scholar
  23. 23.
    Yousfi-Monod, M., Farzindar, A., Lapalme, G.: Supervised machine learning for summarizing legal documents. In: Farzindar, A., Kešelj, V. (eds.) AI 2010. LNCS (LNAI), vol. 6085, pp. 51–62. Springer, Heidelberg (2010).  https://doi.org/10.1007/978-3-642-13059-5_8CrossRefGoogle Scholar
  24. 24.
    Zhang, P., Koppaka, L.: Semantics-based legal citation network. In: Proceedings of the 11th ICAIL, pp. 123–130 (2007)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Ajay Gupta
    • 2
  • Devendra Verma
    • 2
  • Sachin Pawar
    • 1
  • Sangameshwar Patil
    • 1
  • Swapnil Hingmire
    • 1
  • Girish K. Palshikar
    • 1
  • Pushpak Bhattacharyya
    • 3
  1. 1.TCS ResearchTata Consultancy ServicesPuneIndia
  2. 2.Department of CSEIndian Institute of Technology BombayMumbaiIndia
  3. 3.Indian Institute of Technology PatnaPatnaIndia

Personalised recommendations