IAES International Journal of Artificial Intelligence (IJ-AI)
Vol 2, No 1: March 2013

Legal Documents Clustering and Summarization using Hierarchical Latent Dirichlet Allocation

Ravi kumar Venkatesh (NIE Mysore India)



Article Info

Publish Date
01 Mar 2013

Abstract

In a common law system and in a country like India, decisions made by judges are significant sources of application and understanding of law. Online access to the Indian Legal Judgments in the digital form creates an opportunities and challenges to the both legal community and information technology researchers. This necessitates organizing, analyzing, retrieving relevant judgment and presenting it in a useful manner to the legal community for quick understanding and for taking necessary decision pertaining to a present case. In this paper we propose an approach to cluster legal judgments based on the topics obtained from hierarchical Latent Dirichlet Allocation (hLDA) using similarity measure between topics and documents and to find the summarization of each document using the same topics. The developed topic based clustering model is capable of grouping the legal judgments into different clusters and to generate summarization in effective manner compare to our previous [1] approach.DOI: http://dx.doi.org/10.11591/ij-ai.v2i1.1186

Copyrights © 2013






Journal Info

Abbrev

IJAI

Publisher

Subject

Computer Science & IT Engineering

Description

IAES International Journal of Artificial Intelligence (IJ-AI) publishes articles in the field of artificial intelligence (AI). The scope covers all artificial intelligence area and its application in the following topics: neural networks; fuzzy logic; simulated biological evolution algorithms (like ...