Jim Cowie
- 1 Andersen, P. M., Hayes, P.j., Heuttner, A. K., Schmandt, L. M., and Nirenberg, I. B. Automatic extraction. In Proceedings of the Conference of the Association for Artificial Intelligence (Philadelphia, Penn.). 1986, pp. 1089-1093.Google Scholar
- 2 Aone, C., Blejer, H., Flank, S., McKee, D., Shinn, S. The Murasaki Project: Multilingual natural language understanding. In Proceedings of the DARPA Spoken and Written Language Workshop. 1993.Google ScholarDigital Library
- 3 Ayuso, D., Bobrow, R., McLaughlin, D., McLeer, M., Ramshaw, L., Schwartz, R., and Weishedal, R. Towards understanding text with a very large vocabulary. In Proceedings of the DARPA Spoken and Written Language Workshop. (Hidden Valley, Penn.) Morgan Kaufmann, 1990, pp. 354-358. Google ScholarDigital Library
- 4 Commun. ACM 35. Special Section on Information Filtering, Terry, D. and Loeb, S., Eds., (Dec. 1992), 26-81. Google ScholarDigital Library
- 5 Ciravegna, F., Campia, P., and Colognese, A. Knowledge extraction from texts by SINTESI. In Proceedings of the 14th International Conference on Computational Linguistics (COLING92) (Nantes, France) 1992, pp. 1244-1248. Google ScholarDigital Library
- 6 Cowie, J.R. Automatic analysis of descriptive texts. In ACL Proceedings, Conference on Applied Natural Language Processing (Santa Monica, Calif.), 1983, pp. 117-123. Google ScholarDigital Library
- 7 DARPA. Proceedings of the 3d Message Understanding Conference (MUC-3) (San Diego, Calif.), Morgan Kaufmann, 1991.Google Scholar
- 8 DARPA. Proceedings of the 4th Message Understanding Conference (MUC-4) McLean, Va., Morgan Kaufmann, 1992.Google Scholar
- 9 DARPA. Proceedings of the Tipster Text Program (Phase l) Fredricksburg, Va., Morgan Kaufmann, 1993.Google Scholar
- 10 DaSilva, G. and Dwiggins, D. Towards a Prolog text grammar. SIGART 72 (1980).Google Scholar
- 11 DeJong, G. F. Prediction and substantiation: A new approach to natural language processing. Cognitive Sci., 3 (1979), 251-273.Google ScholarCross Ref
- 12 DeJong, G. F. An overview of the FRUMP system. In Strategies for Natural Language Processing. W.G. Lehnert and M.H. Ringle, eds. Erlbaum, Hillsdale, N.J., 1982, pp. 149-176.Google Scholar
- 13 Delannoy, J.F., Feng, C., Matwin, S., and Szpakowicz, S. Knowledge extraction from text: Machine learning for text-to-rule translation. In Proceedings of the Workshop on Machine Learning Techniques and Text Analysis, ECML93 (Vienna). 1993.Google Scholar
- 14 Hahn, U. On text coherence parsing. In Proceedings of the 14th International Conference on Computational Linguistics (COLING92) (Nantes, France). 1992, pp. 25-31. Google ScholarDigital Library
- 15 Jacobs, P.S. and Rau, L.F. SCISOR: Extracting information from on-line news. Commun. ACM 33, 11 (1990), 88-97. Google ScholarDigital Library
- 16 Lehnert, W. and Sundheim, B. A performance evaluation of text analysis technologies. AIMag. 12, 3 (1991), 81-94. Google ScholarDigital Library
- 17 Lenat, D.B. and Guha, R.V. Building Large Knowledge-Based Systems: Representations and Inference in the CYC Project. Addison-Wesley, Reading, Mass., 1989. Google ScholarDigital Library
- 18 Lytinen, S. and Gershman, A. ATRANS: Automatic processing of money transfer messages. In Proceedings of the 5th National Conference of the American Association for Artificial Intelligence. IEEE Computer Society Press, 1993, pp. 93-99.Google Scholar
- 19 Matwin, S. and Szpakowicz, S. Text analysis: How can machine learning help? In Proceedings of the 1st Conference of the Pacific Association for Computational Linguistics (PA CLING) (Vancouver, Canada). 1993, pp. 33-42.Google Scholar
- 20 Mellish, C., Allport, A., Evans, R., Cahill, L.J., Gaizauskas, R., and Walker, J. The TIC message analyzer. Tech. Rep. CSRP 225, Univ. of Sussex, 1992.Google Scholar
- 21 Rau, L. Extracting company names from text. In Proceedings of the 7th Conference on Artificial Intelligence Applications (Miami Beach, Fla.). 1991.Google ScholarCross Ref
- 22 Riloff, E. and Lehnert, W. Classifying texts using relevancy signatures. In Proceedings of the 10th National Conference of the American Association for Artificial Intelligence (San Jose, Calif.). 1992, pp. 329-334.Google ScholarDigital Library
- 23 Sager, N. Natural Language Information Processing: A Computer Grammar of English and its Applications??. Addison-Wesley, Reading, Mass., 1981. Google ScholarDigital Library
- 24 Sundheim, B.M. and Chinchor, N.A. Survey of the message understanding conferences. In Proceedings of the DARPA Spoken and Written Language Workshop. 1993.Google ScholarDigital Library
- 25 Zarri, G.P. Automatic representation of the semantic relationships corresponding to a French surface expression. In ACL Proceedings, Conference on Applied Natural Language Processing (Santa Monica, Calif.). ACL, 1983, pp. 143-147. Google ScholarDigital Library
Index Terms
- Information extraction
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Reviews
Richard L. Frautschi
The authors address the problem of the rising volume of text data available through electronic media and the difficulty of processing these data within feasible time limits. As a retrieval and filtering strategy, information extraction (IE) reduces raw natural language or real world texts to kernels of relevancy. Using recent Message Understanding Conferences, the authors note signs of progress in the daunting task of isolating pertinent and accurate information at low cost and high speed. For example, the New Mexico State University extraction system for Japanese microelectronics processes 100 texts in 30 minutes, versus 20 hours for a human analyst. The crux of the challenge appears to be reconciling subject relevance through “rules” with automated, trainable machines. Preprocessing (such as partial parsing or tagging) may accelerate development cycles and reduce expense, allowing more time for data analysis and internal evaluations. But again, what kind and how much__?__ Finally, the authors emphasize an increased use of statistically based software (such as Markov chains) as a training strategy viable for large corpora, human-tagged texts, and machine-readable dictionaries.
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