Nitin Ramrakhiyani
ABSTRACT
In this work, we describe a system for classification of propositions from legal judgements of the Supreme Court of India. The system was submitted for participation to the Information Access in the Legal Domain track at the Forum for Information Retrieval Evaluation (FIRE) 2013. The system uses a multi-class Maximum Entropy classifier and various specially designed features to capture the underlying characteristics of the legal propositions. The best performing feature set was chosen by 10-fold cross-validation over the training set. The system achieved an accuracy of 65.03% on the training set and an accuracy of 51.02% on the test set.
- A. L. Berger, V. J. D. Pietra, and S. A. D. Pietra. A maximum entropy approach to natural language processing. Computational linguistics, 22(1):39--71, 1996. Google Scholar
Digital Library
- P. R. Cohen and A. E. Howe. How evaluation guides ai research: The message still counts more than the medium. AI Magazine, 9(4):35, 1988. Google ScholarDigital Library
- B. Hachey and C. Grover. Sentence classification experiments for legal text summarisation. In Proceedings of the 17th Annual Conference on Legal Knowledge and Information Systems (Jurix), 2004.Google Scholar
- C. D. Manning, M. Surdeanu, J. Bauer, J. Finkel, S. J. Bethard, and D. McClosky. The Stanford CoreNLP natural language processing toolkit. In Proceedings of 52nd Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pages 55--60, 2014.Google ScholarCross Ref
- Wikipedia. Supreme Court of India, 2014. {Online; Accessed 25-June-2014}.Google Scholar
Index Terms
- A System for Classification of Propositions of the Indian Supreme Court Judgements
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