Michael L. Mauldin
- 1.D. C. Blair and M. E. Maron. "An Evaluation of Retrieval Effectiveness for a Full-Text Document-Retrieval System." Comm. ACM 28, 3 (March 1985), 289-299. Google Scholar
Digital Library
- 2.J. G. Carbonell, W. M. Boggs, M. L. Mauldin and P. G. Anick. "The XCALIBUR Project, A Natural Language Interface to Expert Systems." Proceedings of the Eighth International Joint Conference on Artificial Intelligence, 1983. (IJCAI-83).Google Scholar
- 3.W. B. Croft. "Approaches to Intelligent Information Retrieval." Information Processing and Management 23, 4 (1987), 249-254. Google ScholarDigital Library
- 4.S. Deerwester, S. T. Dumais, G. W. Furnas, T. K. Landauer and R. Harshman. "indexing by Latent Semantic Analysis." Journal of the American Society for Information Science 41, 6 (September 1990), 391-407.Google ScholarCross Ref
- 5.G. F. DeJong. "Prediction and Substantiation: a New Approach to Natural Language Processing." Cognitive Science 3 (September 1979).Google Scholar
- 6.G. F. DeJong. "An Overview of the FRUMP System." In Strategies for Natural Language Processing, W. G. Lehnert and M. H. Ringle, Ed., Lawrence Erlbaum Associates, Hillsdale, NJ, 1982, ch. 5, pp. 149-176.Google Scholar
- 7.G. W. Furnas, S. Deerwester, S. T. Dumais, T. K. Landauer, R. A. Harshman, L. A. Streeter and K. E. Lochbaum. "Information Retrieval using a Singular Value Decomposition Model of Latent Semantic Structure." Proceedings of the Eleventh Annual International ACMSIGIR Conference on Research and Development in Information Retrieval, June, 1988, pp. 465-480. (SIGIR-88). Google ScholarDigital Library
- 8.P. S. Jacobs and L. F. Rau. "SCISOR: Extracting Informarion from On-line News." Comm. ACM 33, 11 (November 1990), 88-97. Google ScholarDigital Library
- 9.B. Katz. Using English for Indexing and Retrieving. A. I. Memo 1096, Massachusetts Institute of Technology, October, 1988. A shorter version of this paper appears in the Proceedings of the Conference on User-oriented Content-based Text and Image Handling, RIAO 1988. Google ScholarDigital Library
- 10.R. Krovetz and W. B. Croft. "Word Sense Disambiguation Using Machine-Readable Dictionaries." Proceedings of the Twelfth Annual International A CMSIGIR Conference on Research and Development in Information Retrieval, June, 1989, pp. 127-136. (SIGIR-89). Google ScholarDigital Library
- 11.D. D. Lewis, W. B. Croft and N. Bhandaru. "Language-Oriented Information Retrieval." International Journal of Intelligent Systems 4, 3 (Fall 1989).Google ScholarDigital Library
- 12.M. L. Mauldin. Information Retrieval by Text Skimming. Ph.D. Th., School of Computer Science, Carnegie Mellon University, August 1989. Google ScholarDigital Library
- 13.E. Nyberg. The FrameKit User's Guide. Center for Machine Translation, Carnegie Mellon University, 1988.Google Scholar
- 14.Y. Ravin. "Disambiguating and Interpreting Verb Definitions." Proceedings of the 28th Annual Meeting of the Association for Computational Linguistics, June, 1990, pp. 260-267. (ACL-90). Google ScholarDigital Library
- 15.G. Salton, E. A. Fox and E. Voorhees. "Advanced Feedback Methods in Information Retrieval." Journal of the American Society for Information Science 36, 3 (1985), 200-210. Google ScholarDigital Library
- 16.D. R. Swanson. "Historical Note: Information Retrieval and the Future of an Illusion." Journal of the American Society for Information Science 39, 2 (1989), 92-98.Google Scholar
- 17.Y.-C. Wang, J. Vandendorpe and M. Evens. "Relational Thesauri in Information Retrieval." Journal of the American Society for Information Science 36, 1 (1985), 15-27. Google ScholarDigital Library
Index Terms
- Retrieval performance in Ferret a conceptual information retrieval system
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