Using cases to represent context for text classification

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Using cases to represent context for text classification

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Conference on Information and Knowledge Management archive
Proceedings of the second international conference on Information and knowledge management table of contents

Washington, D.C., United States

Pages: 105 - 113

Year of Publication: 1993

ISBN:0-89791-626-3

Author

Ellen Riloff

Department of Computer Science, University of Massachusetts, Amherst, MA

Sponsors

Inst Soc for Comp & Applic : Inst Soc for Comp & Applic
SIGART: ACM Special Interest Group on Artificial Intelligence
SIGIR: ACM Special Interest Group on Information Retrieval

Publisher

ACM New York, NY, USA

Bibliometrics

Downloads (6 Weeks): 0, Downloads (12 Months): 19, Citation Count: 1

Additional Information:

references cited by index terms collaborative colleagues peer to peer

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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.

	1	Kevin D. Ashley, Modeling Legal Arguments: Reasoning with Cases and Hypotheticals, MIT Press, Cambridge, MA, 1991
	2	Hammond, K. 1986. CHEF: A Model of Case-Based Planning. In Proceed~g$ of the Fifth National Conference on Artificial Intelligence, pp. 267-271. Morgan Kaufmann.
	3	Lehnert, W. G. 1987. Case-B~ Problem Solving with a Large Knowledge Base of Learned Cases. In Proceedings of the Sixth National Conference on Artificial Intelligence, pp. 301-306. Morgan Kaufmann.
	4	Lehnert, W. G. 1991. Symbolic/Subsymbolic Sentence Analysis: Exploiting the Best of Two Worlds. In Advances in Connectionist and Neural Computation Theory. (Eds: J. Pollack and J. Bamden), pp. 135-164. Norwood, NJ. Ablex Publishing.
	5	W. Lehnert , C. Cardie , D. Fisher , J. McCarthy , E. Riloff , S. Soderland, University of Massachusetts: description of the CIRCUS system as used for MUC-4, Proceedings of the 4th conference on Message understanding, June 16-18, 1992, McLean, Virginia [doi>10.3115/1072064.1072105]
	6	Proceedings of the Fourth Message Understanding Conference. 1992. San Mateo, CA. Morgan Kaufmann.
	7	Riloff, E. 1993. Automatically Constructing a Dictionary for Information Extraction Tasks. In Proceedings of the Eleventh National Conference on ArtO~cial Intelligence, pp. 811-816. AAAI Pressf~e MIT Press.
	8	Riloff, E. and Lehnert, W. 1992. Classifying Texts Using Relevancy Signatures. In Proceedings of the Tenth National Conference on Artificial Intelligence, pp. 329-334. AAAI PressfI'he MIT Press.
	9	Riloff, E. and Lehncrt, W. 1993. Automated Dictionary Construction for Information Extraction from Text. In Proceedings of the Ninth IEEE Conference on Artificial Intelligence for Applications., pp. 93-99. ll~l~ Computer Society Press.
	10	Gerard Salton, Automatic text processing: the transformation, analysis, and retrieval of information by computer, Addison-Wesley Longman Publishing Co., Inc., Boston, MA, 1989
	11	Craig Stanfill , David Waltz, Toward memory-based reasoning, Communications of the ACM, v.29 n.12, p.1213-1228, Dec. 1986 [doi>10.1145/7902.7906]

CITED BY

Ellen Riloff , Wendy Lehnert, Information extraction as a basis for high-precision text classification, ACM Transactions on Information Systems (TOIS), v.12 n.3, p.296-333, July 1994

INDEX TERMS

Primary Classification:
H. Information Systems
H.3 INFORMATION STORAGE AND RETRIEVAL

Additional Classification:
I. Computing Methodologies
I.2 ARTIFICIAL INTELLIGENCE
I.2.7 Natural Language Processing
Subjects: Text analysis
I.5 PATTERN RECOGNITION
I.5.2 Design Methodology
Subjects: Classifier design and evaluation

General Terms:
Algorithms, Design, Documentation, Experimentation, Human Factors, Languages, Management, Measurement, Performance, Theory

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Ellen Riloff: colleagues

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