Analyzing the Extraction of Relevant Legal Judgments using Paragraph-level and Citation Information

Building efficient search systems to extract relevant information from a huge volume of legal judgments is a research issue. In the literature, efforts are being made to build efficient search systems in the legal domain by extending information retrieval approaches. We are making efforts to investigate improved approaches to extract relevant legal judgments for a given input judgment by exploiting text and citation information of legal judgments. Typically, legal judgments are very large text documents and contain several intricate legal concepts. In this paper, we analyze how the paragraphlevel and citation information of the judgments could be exploited for retrieving relevant legal judgments for the given judgment. In this paper, we have proposed improved ranking approach to find the relevant legal judgments of a given judgment based on the similarity between the paragraphs of the judgments by employing Okapi retrieval model and citation information. The user evaluation study on legal judgments data set delivered by Supreme Court of India shows that the proposed approach improves the ranking performance over the baseline approach. Overall, the analysis shows that there is a scope to exploit the paragraph-level and citation information of the judgments to improve the search performance.

[1]  S Sendhilkumar,et al.  Citation Semantic Based Approaches to Identify Article Quality , 2013 .

[2]  M. M. Kessler.  Bibliographic coupling between scientific papers , 1963 .

[3]  Tao Tao,et al.  A formal study of information retrieval heuristics , 2004, SIGIR '04.

[4]  Berthier Ribeiro-Neto,et al.  Concept-based ranking: a case study in the juridical domain , 2004, Inf. Process. Manag..

[5]  Yi-Hung Liu,et al.  A text mining approach to assist the general public in the retrieval of legal documents , 2013, J. Assoc. Inf. Sci. Technol..

[6]  Maristella Agosti,et al.  A two-level hypertext retrieval model for legal data , 1991, SIGIR '91.

[7]  Jaideep Srivastava,et al.  WICER: a weighted inter-cluster edge ranking for clustered graphs , 2005, The 2005 IEEE/WIC/ACM International Conference on Web Intelligence (WI'05).

[8]  M. Saravanan,et al.  Improving legal information retrieval using an ontological framework , 2009, Artificial Intelligence and Law.

[9]  G Salton,et al.  Automatic Analysis, Theme Generation, and Summarization of Machine-Readable Texts , 1994, Science.

[10]  Bao-Quoc Ho,et al.  A Vietnamese Question Answering System in Vietnam's Legal Documents , 2014, CISIM.

[11]  Chris Buckley,et al.  Pivoted Document Length Normalization , 1996, SIGIR Forum.

[12]  Stephen E. Robertson,et al.  GatfordCentre for Interactive Systems ResearchDepartment of Information , 1996 .

[13]  Gerard Salton,et al.  A vector space model for automatic indexing , 1975, CACM.

[14]  Howard R. Turtle.  Text retrieval in the legal world , 1995, Artificial Intelligence and Law.

[15]  Pompeu Casanovas,et al.  Semantic enhancement for legal information retrieval: Iuriservice performance , 2007, ICAIL.

[16]  Paul Zhang,et al.  Semantics-based legal citation network , 2007, ICAIL.

[17]  Burkhard Schafer,et al.  Concept and Context in Legal Information Retrieval , 2008, JURIX.

[18]  Sushanta Kumar,et al.  Similarity analysis of legal judgments , 2011, Bangalore Compute Conf..

[19]  Jack G. Conrad,et al.  Legal document clustering with built-in topic segmentation , 2011, CIKM '11.

[20]  P. Krishna Reddy,et al.  Text and Citations Based Cluster Analysis of Legal Judgments , 2015, MIKE.

[21]  Paulo Quaresma,et al.  A question-answering system for Portuguese juridical documents , 2005, ICAIL '05.

[22]  M. Saravanan,et al.  Improving Legal Document Summarization Using Graphical Models , 2006, JURIX.

[23]  Sergey Brin,et al.  The Anatomy of a Large-Scale Hypertextual Web Search Engine , 1998, Comput. Networks.

[24]  P. Krishna Reddy,et al.  Finding Similar Legal Judgements under Common Law System , 2013, DNIS.