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Finding factors: learning to classify case opinions under abstract fact categories
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Source International Conference on Artificial Intelligence and Law archive
Proceedings of the 6th international conference on Artificial intelligence and law table of contents
Melbourne, Australia
Pages: 123 - 131  
Year of Publication: 1997
ISBN:0-89791-924-6
Authors
Stefanie Brüninghaus  University of Pittsburgh, Learning Research and Development Center, Intelligent Systems Program and School of Law, Pittsburgh, PA
Kevin D. Ashley  University of Pittsburgh, Learning Research and Development Center, Intelligent Systems Program and School of Law, Pittsburgh, PA
Sponsors
SIGART: ACM Special Interest Group on Artificial Intelligence
IAAIL : Intl Asso for Artifical Intel & Law
UMIACS : U of MD Inst for Advanced Comp Studies
University of Melbourne : University of Melbourne
Publisher
ACM  New York, NY, USA
Bibliometrics
Downloads (6 Weeks): 4,   Downloads (12 Months): 16,   Citation Count: 10
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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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Aleven, V., and Ashley, K. 1997
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Empirical Support for Winnow and Weighted- Majority based algorithms: results from a calendar-scheduling domain. In Proceedings of the 12th International Conference on Machine Learning.
 
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Callan, J. 1996
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Craven, M.; Freitag, D.; McCallum, A.; Mitchell, T.; Nigam, K.; and Yank Queak, C. 1997
Learning to Extract Symbolic Knowledge from the World Wide Web. Submitted to ML-97.
 
Daniels, J. 1996
Retrieval of Passages for Information Reduction. PhD Proposal, University of Massachusetts, Amherst.
 
Frankes, W., and Baeza-Yates, R 1992
Information Retrieval - Data Structures E4 Algorithms. Prentice-Hall.
 
Freitag, D. 1997
Machine Learning for Information Extraction from Online Documents. PhD Proposal, Carnegie-Mellon University, Pittsburgh.
 
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Applying winnow to contextsensitive spelling correction. In Proceedings of the 13th lnterna. tional Conference on Machine Learning.
 
Joachims, T. 1996
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Kivinen, J., and Warmuth, M. 1994
Exponentiated Gradient versus Gradient Descent for Linear Predictors. Technical report, University of California Santa Clara.
 
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Explanation- Based Learning for Mobile Robot Perception. In Proceedings of the Eleventh International Conference on Machine Learning.
 
Mitchell, T. 1997
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Pazzani, M.; Muramatsu, J.; and Bilsus, D. 1996
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CITED BY  10

Collaborative Colleagues:
Stefanie Brüninghaus: colleagues
Kevin D. Ashley: colleagues