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Machine Learning in Subject Classification

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Part of the book series: Workshops in Computing ((WORKSHOPS COMP.))

Abstract

This paper describes the application of Machine Learning (ML) techniques to the problem of Information Retrieval. Specifically, it presents a system which incorporates machine learning techniques in determining the subject(s) of a piece of text. This system is part of a much larger information management system which provides software support for the creation, management and querying of very large information bases. The information stored in these information bases is typically technical manuals, technical reports or other full-text documents. This paper gives a brief description of the overall system, followed by an overview of Machine Learning and a summary of a number of ML systems. We then describe the classification algorithm used in the system. Finally, the learning module, which will be incorporated into the classification algorithm, is described.

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References

  1. Smart, G., March 1990, Year One: The Results so far, CRIAS Bregnerodvej 144, DK-3460, Birkerod, Denmark.

    Google Scholar 

  2. Smart, G., March 1989, SIMPR: An Introductory Description, CRIAS Bregnerodvej 144, DK-3460, Birkerod, Denmark.

    Google Scholar 

  3. The Subject Approach to Information, 3rd Ed., Bingley Ltd., 1977.

    Google Scholar 

  4. Aitchison, J., Gilchrist, A., Thesaurus Construction. ASLIB, London, 1987.

    Google Scholar 

  5. Sharif, C., Subject Classification Research, University of Strathclyde, September 1989.

    Google Scholar 

  6. McCarthy, J., President's Quarterly Message: AI needs more Emphasis on Basic Research, AI Magazine, Vol 4, 1983.

    Google Scholar 

  7. Schank, R.C., The Current State of AI: One Man's Opinion, AI Magazine, Vol 4, 1983.

    Google Scholar 

  8. Luger, G.F., Stubblefield W.A., Artificial Intelligence and the Design of Expert Systems, Benjamin/Cummings Publishing Company, 1989.

    Google Scholar 

  9. Michalski, R.S., Understanding the Nature of Learning. In Michalski, R.S., Carbonell, J.G., Mitchell, T.M., eds. Chap 1, Machine Learning, An AI Approach, Vol 2, Morgan Kaufmann, 1986.

    Google Scholar 

  10. DeJong, G., An Approach to Learning from Observation, Machine Learning, Vol 1, Morgan Kaufmann 1983.

    Google Scholar 

  11. Kavanagh, I., Machine Learning: A Survey, University College Dublin, SIMPRUCD-1989-8. 03, October 1989.

    Google Scholar 

  12. Stepp, R.E., Michalski, R.S., Conceptual Clustering: Inventing Goal Oriented Classifications of Structured Objects, In Michalski, R.S., Carbonell, J.G., Mitchell, T.M., eds. Chap 1, Machine Learning, An AI Approach, Vol 2, Morgan Kaufmann, 1986. 1986.

    Google Scholar 

  13. Quinlan, J.R., Learning Efficient Classification Procedures and their Application to Chess End Games. In Michalski, R.S., Carbonell, J.G. and Mitchell, T.M., eds. Machine Learning, Chapter 15, pages 463–482, Morgan Kaufmann 1983.

    Google Scholar 

  14. Quinlan, J.R., Simplifying Decision trees,International Journal of Man-Machine Studies 27, 1987.

    Google Scholar 

  15. Michalski, R.S., A theory and methodology of inductive learning. In Michalski, R.S., Carbonell, J.G. and Mitchell, T.M., eds. Machine Learning, Chapter 4, pages 83–134, Morgan Kaufmann, 1983.

    Google Scholar 

  16. Laird, J.E., Rosenbloom, P.S., Newell, A., Chunking in SOAR: the anatomy of a general learning mechanism. Machine Learning, 1 (1): 11–46, 1986.

    Google Scholar 

  17. Rosenbloom, P.S., Newell A., The Chunking of Goal Hierarchies: A Generalized Model of Practice, In Machine Learning: An Artificial Intelligence Approach, Volume 2, 1986.

    Google Scholar 

  18. Ward, C. et al, The Heading Exercise, Department of Computer Science, University College Dublin, SIMPR-UCD-1990–16.4.4.,. April 1990.

    Google Scholar 

  19. Simon, Herbert A., Why should machines learn? In Ryszard S. Michalski, Jamie G. Carbonell and Tom Mitchell, editors, Machine Learning, chapter 2, pages 2438, Morgan Kaufmann, 1983.

    Google Scholar 

  20. Kodratoff, Y.,Introduction to Machine Learning, Pitman, 1988.

    Google Scholar 

  21. Ellman, T., Explanation-Based Learning: A Survey of Programs and Perspectives, ACM Computing Surveys, Vol 21, No 2, June 1989.

    Google Scholar 

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© 1991 Springer-Verlag Berlin Heidelberg

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Ward, C., Kavanagh, I., Dunnion, J. (1991). Machine Learning in Subject Classification. In: McTear, M.F., Creaney, N. (eds) AI and Cognitive Science ’90. Workshops in Computing. Springer, London. https://doi.org/10.1007/978-1-4471-3542-5_4

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  • DOI: https://doi.org/10.1007/978-1-4471-3542-5_4

  • Publisher Name: Springer, London

  • Print ISBN: 978-3-540-19653-2

  • Online ISBN: 978-1-4471-3542-5

  • eBook Packages: Springer Book Archive

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