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Concept extraction from legal cases: the use of a statistic of coincidence
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Source International Conference on Artificial Intelligence and Law archive
Proceedings of the 9th international conference on Artificial intelligence and law table of contents
Scotland, United Kingdom
SESSION: Short paper session table of contents
Pages: 142 - 146  
Year of Publication: 2003
ISBN:1-58113-747-8
Authors
Marie-Francine Moens  Katholieke Universiteit Leuven, Belgium
Roxana Angheluta  Katholieke Universiteit Leuven, Belgium
Sponsors
: The Joseph Bell Centre for Forensic Statistics and Legal Reasoning
: West Group, Thomson Legal & Regulatory
: The University of Edinburgh School of Law
SIGART: ACM Special Interest Group on Artificial Intelligence
: The International Association for Artificial Intelligence and Law
Publisher
ACM  New York, NY, USA
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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

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
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Collaborative Colleagues:
Marie-Francine Moens: colleagues
Roxana Angheluta: colleagues