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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
The Supreme Court of India Judgments. http://www.liiofindia.org/in/cases/cen/INSC/
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)
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)
Brin, S., Page, L.: The anatomy of a large-scale hypertextual web search engine. Comput. Netw. ISDN Syst. 30(1–7), 107–117 (1998)
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)
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)
Dean, J., Henzinger, M.R.: Finding related pages in the world wide web. Comput. Netw. 31(11–16), 1467–1479 (1999)
Hartigan, J.A., Wong, M.A.: Algorithm as 136: a k-means clustering algorithm. Appl. Stat. 28, 100–108 (1979)
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)
Kleinberg, J.M.: Authoritative sources in a hyperlinked environment. J. ACM 46(5), 604–632 (1999)
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)
Kumar, R., Raghavan, P., Rajagopalan, S., Tomkins, A.: Trawling the web for emerging cyber-communities. Comput. Netw. 31(11–16), 1481–1493 (1999)
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)
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)
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)
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)
Porter, M.: An algorithm for suffix stripping. Program Electron. Libr. Inf. Syst. 14(3), 130–137 (1980)
Salton, G., Wong, A., Yang, C.S.: A vector space model for automatic indexing. Commun. ACM 18(11), 613–620 (1975)
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)
Salton, G., Buckley, C.: Term-weighting approaches in automatic text retrieval. Inf. Process. Manag. 24(5), 513–523 (1988)
Saravanan, M., Ravindran, B., Raman, S.: Improving legal document summarization using graphical models. In: Proceedings of the JURIX 2006, pp. 51–60. IOS (2006)
Schwarz, G.: Estimating the dimension of a model. Ann. Stat. 6(2), 461–464 (1978)
Thompson, P.: Automatic categorization of case law. In: Proceedings of the 8th International Conference on Artificial Intelligence and Law, pp. 70–77. ACM (2001)
Xu, R., Wunsch II, D.: Survey of clustering algorithms. Trans. Neur. Netw. 16(3), 645–678 (2005)
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)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-319-26832-3_42
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-26831-6
Online ISBN: 978-3-319-26832-3
eBook Packages: Computer ScienceComputer Science (R0)