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Concept extraction from legal cases: the use of a statistic of coincidence

Published: 24 June 2003 Publication History

Abstract

Effective retrieval of court decisions is important. Automatically identifying legal concepts in the decision texts would be very helpful. In this paper we investigate how a statistics for hypothesis testing, i.e., the likelihood ratio, can help in this task. We describe how this statistic can be used for detecting important multi-term phrases in the case texts, how it can be used to find correlated terms, and how it is a means for feature or topic signature selection in automated case categorization. The technology has been tested upon more than 600 US cases.

References

[1]
R. Baeza-Yates and B. Ribeiro-Neto. Modern Information Retieval. Addison Wesley, Harlow, UK, 1999.
[2]
J. Bing. Performance of legal text retrieval systems: The curse of boole. Law Library Journal, 79:187--202, 1987.
[3]
C. Bishop. Neural Networks for Pattern Recognition. Clarendon Press, Oxford, UK, 1995.
[4]
J. Breuker, A. Elhag, E. Petkov, and R. Winkels. T. Bench-Capon, A. Daskalopulu, and R. Winkels (Eds.), Legal Knowledge and Information Systems, chapter Ontologies for legal information serving and knowledge management, pages 73--82. IOS Press, Amsterdam, 2002.
[5]
S. Brninghaus and K. Ashley. Finding factors: Learning to classify case opinions under abstract fact categories. In Proceedings of the Sixth International Conference on Artificial Intelligence and Law, pages 123--131. ACM, New York, 1997.
[6]
S. Brninghaus and K. Ashley. Toward adding knowledge to learning algorithms for indexing legal cases. In Proceedings of the Seventh International Conference on Artificial Intelligence and Law, pages 7--17. ACM, New York, 1999.
[7]
S. Deerwester, S. Dumais, G. Furnas, T. Landauer, and R. Harshman. Indexing by latent semantic analysis. Journal of the American Society for Information Science, 41 (6):391--407, 1990.
[8]
J. Dick. Representation of legal text for conceptual retrieval. In Proceedings of the Second International Conference on Artificial Intelligence and Law, pages 244--253. ACM, New York, 1989.
[9]
T. Dunning. Accurate methods for the statistics of surprise and coincidence. Computational Linguistics, 19:61--74, 1993.
[10]
M. Gruninger and J. Lee. Ontology applications and design. Communications of the ACM, 45 (2):39--41, 2002.
[11]
C. Hafner. An information retrieval system based on a computer model of legal knowledge. UMI Research Press, Ann Arbor, MI, 1981.
[12]
C.-Y. Lin and E. Hovy. The automated acquisition of topic signatures for text summarization. In Proceedings of the COLING Conference, 2000. http://www.isi.edu/natural-language/people/hovy/publications.html.
[13]
C. Manning and H. Schtze. Foundations of Statistical Natural Language Processing. MIT Press Cambridge, MA, 1999.
[14]
L. McCarty. Intelligent legal information systems: problems and prospects. In C. Campbell (Ed.), Data Processing and the Law. Sweet & Maxwell, London, pages 125--151, 1984.
[15]
T. Mitchell. Machine Learning. McGraw-Hill, Boston, MA, 1997.
[16]
M.-F. Moens. Automatic indexing and abstracting of document texts. (The Kluwer International Series on Information Retrieval 6). Kluwer Academic Publishers, Boston, MA., 2000.
[17]
M.-F. Moens, R. Angheluta, and R. De Busser. In W. Abramowicz (Ed.), Knowledge Based Information Retirieval and Filtering, chapter Summarization of texts found on the World Wide Web. Kluwer Academic Publishers, Boston (in press), 2003.
[18]
M.-F. Moens and R. De Busser. First steps in building a model for the retrieval of court decisions. International Journal of Human-Computer Studies, 57, 5:429--446, 2002.
[19]
E. Rissland, S. D., and M. Friedman. Bankxx: Supporting legal arguments through heuristic retrieval. Artificial Intelligence and Law, 4 (1):1--71, 1996.
[20]
D. Rose and R. Belew. A connectionist and symbolic hybrid for improving legal research. International Journal of Man-Machine Studies, 35 (1):1--33, 1991.
[21]
P. Thompson. Automatic categorization of case law. In Proceedings of the 8th International Conference on Artificial Intelligence and Law, pages 73--82. ACM, New York, 2001.
[22]
R. Winkels, D. Bosscher, A. Boer, and R. Hoekstra. Extended conceptual retrieval. In Legal Knowledge and Information Systems: Jurix 2000: The Thirteenth Annual Confernece, pages 85--97. IOS Press, Amsterdam, 2000.

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cover image ACM Conferences
ICAIL '03: Proceedings of the 9th international conference on Artificial intelligence and law
June 2003
304 pages
ISBN:1581137478
DOI:10.1145/1047788
  • Conference Chair:
  • John Zeleznikow,
  • Program Chair:
  • Giovanni Sartor
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Publication History

Published: 24 June 2003

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Author Tags

  1. concept extraction
  2. conceptual information retrieval
  3. ontology building

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Overall Acceptance Rate 69 of 169 submissions, 41%

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  • (2015)LEXAExpert Systems with Applications: An International Journal10.1016/j.eswa.2015.04.02242:17(6391-6407)Online publication date: 1-Oct-2015
  • (2015)Extracting indices from Japanese legal documentsArtificial Intelligence and Law10.1007/s10506-015-9168-823:4(315-344)Online publication date: 1-Dec-2015
  • (2008)Knowledge Element Extraction for Knowledge-Based Learning Resources OrganizationAdvances in Web Based Learning – ICWL 200710.1007/978-3-540-78139-4_10(102-113)Online publication date: 2008
  • (2007)Knowledge element extraction for knowledge-based learning resources organizationProceedings of the 6th international conference on Advances in web based learning10.5555/2170285.2170298(102-113)Online publication date: 15-Aug-2007
  • (2007)Summarizing court decisionsInformation Processing and Management: an International Journal10.1016/j.ipm.2007.01.00543:6(1748-1764)Online publication date: 1-Nov-2007
  • (2004)Summarizing texts at various levels of detailCoupling approaches, coupling media and coupling languages for information retrieval10.5555/2816272.2816327(597-609)Online publication date: 26-Apr-2004

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