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Text and Citations Based Cluster Analysis of Legal Judgments

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Mining Intelligence and Knowledge Exploration (MIKE 2015)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9468))

Abstract

Developing efficient approaches to extract relevant information from a collection of legal judgments is a research issue. Legal judgments contain citations in addition to text. It can be noted that the link information has been exploited to build efficient search systems in web domain. Similarly, the citation information in legal judgments could be utilized for efficient search. In this paper, we have proposed an approach to find similar judgments by exploiting citations in legal judgments through cluster analysis. As several judgments have few citations, a notion of paragraph link is employed to increase the number of citations in the judgment. User evaluation study on the judgment dataset of Supreme Court of India shows that the proposed clustering approach is able to find similar judgments by exploiting citations and paragraph links. Overall, the results show that citation information in judgments can be exploited to establish similarity between judgments.

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References

  1. The Supreme Court of India Judgments. http://www.liiofindia.org/in/cases/cen/INSC/

  2. Al-Kofahi, K., Tyrrell, A., Vachher, A., Jackson, P.: A machine learning approach to prior case retrieval. In: Proceedings of the 8th International Conference on Artificial Intelligence and Law, pp. 88–93. ACM (2001)

    Google Scholar 

  3. Bottou, L., Bengio, Y.: Convergence properties of the k-means algorithms. In: Tesauro, G., et al. (eds.) Advances in Neural Information Processing Systems 7, pp. 585–592. MIT, Cambridge (1995)

    Google Scholar 

  4. Brin, S., Page, L.: The anatomy of a large-scale hypertextual web search engine. Comput. Netw. ISDN Syst. 30(1–7), 107–117 (1998)

    Article  Google Scholar 

  5. Calado, P., Cristo, M., Moura, E., Ziviani, N., Ribeiro-Neto, B., Gonalves, M.A.: Combining link-based and content-based methods for web document classification. In: Proceedings of the 12th CIKM, pp. 394–401. ACM (2003)

    Google Scholar 

  6. Conrad, J.G., Al-Kofahi, K., Zhao, Y., Karypis, G.: Effective document clustering for large heterogeneous law firm collections. In: Proceedings of the 10th International Conference on Artificial Intelligence and Law, pp. 177–187. ACM (2005)

    Google Scholar 

  7. Dean, J., Henzinger, M.R.: Finding related pages in the world wide web. Comput. Netw. 31(11–16), 1467–1479 (1999)

    Article  Google Scholar 

  8. Hartigan, J.A., Wong, M.A.: Algorithm as 136: a k-means clustering algorithm. Appl. Stat. 28, 100–108 (1979)

    Article  MATH  Google Scholar 

  9. He, X., Zha, H., Ding, C.H., Simon, H.D.: Web document clustering using hyperlink structures. Comput. Stat. Data Anal. 41(1), 19–45 (2002)

    Article  MATH  MathSciNet  Google Scholar 

  10. Kleinberg, J.M.: Authoritative sources in a hyperlinked environment. J. ACM 46(5), 604–632 (1999)

    Article  MATH  MathSciNet  Google Scholar 

  11. Knoth, P., Novotny, J., Zdrahal, Z.: Automatic generation of inter-passage links based on semantic similarity. In: Proceedings of the 23rd International Conference on Computational Linguistics, pp. 590–598. Association for Computational Linguistics (2010)

    Google Scholar 

  12. Kumar, R., Raghavan, P., Rajagopalan, S., Tomkins, A.: Trawling the web for emerging cyber-communities. Comput. Netw. 31(11–16), 1481–1493 (1999)

    Article  Google Scholar 

  13. Kumar, S.: Similarity Analysis of Legal Judgments and applying Paragraph-link to Find Similar Legal Judgments. Master’s thesis, International Institute of Information Technology Hyderabad (2014)

    Google Scholar 

  14. Kumar, S., Reddy, P.K., Reddy, V.B., Singh, A.: Similarity analysis of legal judgments. In: Proceedings of 4th Annual ACM COMPUTE 2011, pp. 17:1–17:4. ACM (2011)

    Google Scholar 

  15. Kumar, S., Reddy, P.K., Reddy, V.B., Suri, M.: Finding similar legal judgements under common law system. In: Madaan, A., Kikuchi, S., Bhalla, S. (eds.) DNIS 2013. LNCS, vol. 7813, pp. 103–116. Springer, Heidelberg (2013)

    Chapter  Google Scholar 

  16. Lu, Q., Conrad, J.G., Al-Kofahi, K., Keenan, W.: Legal document clustering with built-in topic segmentation. In: Proceedings of the 20th CIKM, pp. 383–392. ACM (2011)

    Google Scholar 

  17. Porter, M.: An algorithm for suffix stripping. Program Electron. Libr. Inf. Syst. 14(3), 130–137 (1980)

    Article  Google Scholar 

  18. Salton, G., Wong, A., Yang, C.S.: A vector space model for automatic indexing. Commun. ACM 18(11), 613–620 (1975)

    Article  MATH  Google Scholar 

  19. Salton, G., Allan, J., Buckley, C., Singhal, A.: Automatic analysis, theme generation, and summarization of machine-readable texts. In: Card, S.K., Mackinlay, J.D., Shneiderman, B. (eds.) Readings in Information Visualization, pp. 413–418. Morgan Kaufmann Publishers Inc., San Francisco (1999)

    Google Scholar 

  20. Salton, G., Buckley, C.: Term-weighting approaches in automatic text retrieval. Inf. Process. Manag. 24(5), 513–523 (1988)

    Article  Google Scholar 

  21. Saravanan, M., Ravindran, B., Raman, S.: Improving legal document summarization using graphical models. In: Proceedings of the JURIX 2006, pp. 51–60. IOS (2006)

    Google Scholar 

  22. Schwarz, G.: Estimating the dimension of a model. Ann. Stat. 6(2), 461–464 (1978)

    Article  MATH  Google Scholar 

  23. Thompson, P.: Automatic categorization of case law. In: Proceedings of the 8th International Conference on Artificial Intelligence and Law, pp. 70–77. ACM (2001)

    Google Scholar 

  24. Xu, R., Wunsch II, D.: Survey of clustering algorithms. Trans. Neur. Netw. 16(3), 645–678 (2005)

    Article  Google Scholar 

  25. Zhang, P., Koppaka, L.: Semantics-based legal citation network. In: Proceedings of the 11th International Conference on Artificial Intelligence and Law, pp. 123–130. ACM (2007)

    Google Scholar 

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Correspondence to K. Raghav .

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Raghav, K., Balakrishna Reddy, P., Balakista Reddy, V., Krishna Reddy, P. (2015). Text and Citations Based Cluster Analysis of Legal Judgments. In: Prasath, R., Vuppala, A., Kathirvalavakumar, T. (eds) Mining Intelligence and Knowledge Exploration. MIKE 2015. Lecture Notes in Computer Science(), vol 9468. Springer, Cham. https://doi.org/10.1007/978-3-319-26832-3_42

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  • DOI: https://doi.org/10.1007/978-3-319-26832-3_42

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  • Print ISBN: 978-3-319-26831-6

  • Online ISBN: 978-3-319-26832-3

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