Abstract
A major knowledge-engineering bottleneck for information extraction systems is the process of constructing an appropriate dictionary of extraction patterns. AutoSlog is a dictionary construction system that has been shown to substantially reduce the time required for knowledge engineering by learning extraction patterns automatically. However, an open question was whether these extraction patterns were useful for tasks other than information extraction. We describe a series of experiments that show how the extraction patterns learned by AutoSlog can be used for text classification. Three dictionaries produced by AutoSlog for different domains performed well in our text classification experiments.
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References
Carbonell, J. G. 1979. Towards a Self-Extending Parser. In Proceedings of the 17th Meeting of the Association for Computational Linguistics. 3–7.
DeJong, Gerald and Mooney, R. 1986. Explanation-Based Learning: An Alternative View. Machine Learning 1:145–176.
Fisher, D. H. 1987. Knowledge Acquisition Via Incremental Conceptual Clustering. Machine Learning 2:139–172.
Granger, R. H. 1977. FOUL-UP: A Program that Figures Out Meanings of Words from Context. In Proceedings of the Fifth International Joint Conference on Artificial Intelligence. 172–178.
Jacobs, Paul and Rau, Lisa 1990. SCISOR: Extracting Information from On-Line News. Communications of the ACM 33(11):88–97.
Jacobs, P. and Zernik, U. 1988. Acquiring Lexical Knowledge from Text: A Case Study. In Proceedings of the Seventh National Conference on Artificial Intelligence. 739–744.
Kim, J. and Moldovan, D. 1993. Acquisition of Semantic Patterns for Information Extraction from Corpora. In Proceedings of the Ninth IEEE Conference on Artificial Intelligence for Applications, Los Alamitos, CA. IEEE Computer Society Press. 171–176.
Lehnert, W. G. and Sundheim, B. 1991. A Performance Evaluation of Text Analysis Technologies. AI Magazine 12(3):81–94.
Lehnert, W.; Cardie, C.; Fisher, D.; Riloff, E.; and Williams, R. 1991. University of Massachusetts: Description of the CIRCUS System as Used for MUC-3. In Proceedings of the Third Message Understanding Conference (MUC-3), San Mateo, CA. Morgan Kaufmann. 223–233.
Lehnert, W.; Cardie, C.; Fisher, D.; McCarthy, J.; Riloff, E.; and Soderland, S. 1992. University of Massachusetts: MUC-4 Test Results and Analysis. In Proceedings of the Fourth Message Understanding Conference (MUC-4), San Mateo, CA. Morgan Kaufmann. 151–158.
Lehnert, W. 1991. Symbolic/Subsymbolic Sentence Analysis: Exploiting the Best of Two Worlds. In Barnden, J. and Pollack, J., editors 1991, Advances in Connectionist and Neural Computation Theory, Vol. 1. Ablex Publishers, Norwood, NJ. 135–164.
Mitchell, T. M.; Keller, R.; and Kedar-Cabelli, S. 1986. Explanation-Based Generalization: A Unifying View. Machine Learning 1:47–80.
Proceedings of the Third Message Understanding Conference (MUC-3), San Mateo, CA. Morgan Kaufmann.
Proceedings of the Fourth Message Understanding Conference (MUC-4), San Mateo, CA. Morgan Kaufmann.
Proceedings of the Fifth Message Understanding Conference (MUC-5), San Francisco, CA. Morgan Kaufmann.
Quinlan, J. R. 1986. Induction of Decision Trees. Machine Learning 1:80–106.
Riloff, E. and Lehnert, W. 1994. Information Extraction as a Basis for High-Precision Text Classification. ACM Transactions on Information Systems 12(3):296–333.
Riloff, E. and Shoen, J. 1995. Automatically Acquiring Conceptual Patterns Without an Annotated Corpus. In Proceedings of the Third Workshop on Very Large Corpora. 148–161.
Riloff, E. 1993. Automatically Constructing a Dictionary for Information Extraction Tasks. In Proceedings of the Eleventh National Conference on Artificial Intelligence. AAAI Press/The MIT Press. 811–816.
Riloff, E. 1996. An Empirical Study of Automated Dictionary Construction for Information Extraction in Three Domains. Artificial Intelligence. To appear.
Soderland, S.; Fisher, D.; Aseltine, J.; and Lehnert, W. 1995. CRYSTAL: Inducing a conceptual dictionary. In Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence. 1314–1319.
Proceedings of the TIPSTER Text Program (Phase I), San Francisco, CA. Morgan Kaufmann.
Utgoff, P. 1988. ID5: An Incremental ID3. In Proceedings of the Fifth International Conference on Machine Learning. 107–120.
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© 1996 Springer-Verlag Berlin Heidelberg
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Riloff, E. (1996). Using learned extraction patterns for text classification. In: Wermter, S., Riloff, E., Scheler, G. (eds) Connectionist, Statistical and Symbolic Approaches to Learning for Natural Language Processing. IJCAI 1995. Lecture Notes in Computer Science, vol 1040. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-60925-3_53
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DOI: https://doi.org/10.1007/3-540-60925-3_53
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