Ronen Feldman
- 1.Appelt, D. E., Hobbs J., Bear J., Israel D., and Tyson M., 1993. "FASTUS: A Finite-State Processor for Information Extraction from Real-World Text", Proceedings. IJCAI-93, Chambery, France, August 1993.Google Scholar
- 2.Aumann Y., Feldman R., Ben Yehuda Y., Landau D., Lipshtat O., Schler Y.: Circle Graphs: New Visualization Tools for Text-Mining. PKDD 1999: 277-282 Google ScholarDigital Library
- 3.Califf, M. E. and Mooney, R. (1997). Relational learning of pattern-match rules for informationGoogle Scholar
- 4.Cohen. W., "Compiling Prior Knowledge into an Explicit Bias". Working notes of the 1992 AAAI spring symposium on knowledge assimilation. Stanford, CA, March 1992.Google Scholar
- 5.Craven M., DiPasquo D., Freitag D., McCallum A., Mitchell T., Nigam K. and Slattery S. Learning to Extract Symbolic Knowledge from the World Wide Web. Proceedings of the 15th National Conference on Artificial Intelligence (AAAI-98). Google ScholarDigital Library
- 6.Fisher D., Soderland S., McCarthy J., Feng F. and Lehnert W., "Description of the UMass Systems as Used for MUC-6," in Proceedings of the 6th Message Understanding Conference, November, 1995, pp. 127- 140. Google ScholarDigital Library
- 7.Feldman R., Segre A. and Koppel M., "Incremental Refinement of Approximate Domain Theories". Proceedings of the 8th International Conference on Machine Learning, 500-504, Evanston,IL,1991.Google ScholarCross Ref
- 8.Feldman. R., "Probabilistic Revision of Logical Domain Theories". Ph.D Thesis, Computer Science Department, Cornell University, Ithaca NY, February 1993. Google ScholarDigital Library
- 9.Feldman R., Aumann Y., Fresko M., Lipshtat O., Rosenfeld B., Schler Y.: Text Mining via Information Extraction. PKDD 1999: 165-173 Google ScholarDigital Library
- 10.Feldman R., Aumann Y., Zilberstein A., Ben-Yehuda Y. Trend Graphs: Visualizing the Evolution of Concept Relationships in Large Document Collections. PKDD 1998: 38-46 Google ScholarDigital Library
- 11.Feldman R., Fresko M., Kinar Y., Lindell Y., Liphstat O., Rajman M., Schler Y., Zamir O. Text Mining at the Term Level. PKDD 1998: 65-73 Google ScholarDigital Library
- 12.Feldman R., Dagan I., Hirsh H. Mining Text Using Keyword Distributions. JIIS 10(3): 281-300 (1998) Google ScholarDigital Library
- 13.Feldman R., Kl~sgen W., Zilberstein A. Visualization Techniques to Explore Data Mining Results for Document Collections. KDD 1997: 16-23Google Scholar
- 14.Freitag, D. (1998). Multistrategy learning for information extraction. Proceedings of the 15th International ML Conference, 161-169. Google ScholarDigital Library
- 15.Huffman, S. (1996). Learning information extraction patterns from examples. In Wermter, Learning for Natural Language Processing. Berlin: Springer. Google ScholarDigital Library
- 16.Pazzani M. and Kibler D., "The Utility of Knowledge in Inductive Learning". Technical Report, University of California,Irvine, 1990.Google Scholar
- 17.Mahoney J.J. and Mooney R.J., "Combining Neural and Symbolic Learning to Revise Probabilistic Rule Bases". Advances in Neural Information Processing Systems, Vol. 5. Morgan Kaufman, San Mateo, CA, 1993. Google ScholarDigital Library
- 18.Ourston D. and Mooney R. J.,"Changing the rules: A comprehensive approach to theory revision". Proceedings of the Eighth National Conference on Artificial Intelligence, pages 815-820, Boston, MA, 1990.Google Scholar
- 19.Pazzani M. J., "Detecting and Correcting Errors of Omission after Explanation-Based Learning", in Proceedings of the Eleventh International Joint Conference on Artificial Intelligence, pp 713-718, Detroit, Aug 1989.Google Scholar
- 20.Quinlan J.R., "Induction on Decision trees". Machine Learning, 1, 81-106, 1986. Google ScholarDigital Library
- 21.Richards B.L., and Mooney R.J., "First-Order Theory Revision". Proceedings of the 8th International Workshop on Machine Learning, 447-451, Evanston,IL,1991.Google ScholarCross Ref
- 22.Riloff E. and Lehnert W., Information Extraction as a Basis for High-Precision Text Classification, ACM Transactions on Information Systems (special issue on text categorization). Google ScholarDigital Library
- 23.Soderland S., "Learning Information Extraction Rules for Semi-structured and Free Text," Machine Learning Journal, 1999, 1-44. Google ScholarDigital Library
- 24.Towell G.G., Shavlik J. and Noordewier M.O., "Refinement of approximately correct domain theories by knowledge-based neural networks". Proceedings of the Eighth National Conference on Artificial Intelligence, 861-866, Boston, 1990.Google ScholarDigital Library
- 25.Wogulis J., "Revising Relational Domain Theories". Proceedings of the 8th International Workshop on Machine Learning, 462-466, Evanston, IL, 1991.Google Scholar
Index Terms
- A framework for specifying explicit bias for revision of approximate information extraction rules
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