A machine learning experiment to determine part of speech from word-endings

  • Jerzy W. Grzymala-Busse
  • L. John Old
Communications Session 6B Learning and Discovery Systems
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1325)


A file containing the last three characters of words from Roget's Thesaurus was created. Every entry was classified as belonging to one of the five parts of speech: nouns, verbs, adjectives, adverbs, and prepositions. The machine learning system LERS induced rules from this file. The paper describes this experiment. Two interesting regularities of the English language were discovered. Moreover, using a set of rules induced by LERS it is feasible to recognize part of speech of a word on the basis of its last three characters with the expected error rate of 26.71 %.


Learning and knowledge discovery rough set theory Roget's Thesaurus rule induction classification of examples system LERS 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Booker, L. B., Goldberg, D. E., and Holland, J. F.: Classifier systems and genetic algorithms. In Machine Learning. Paradigms and Methods. Carbonell, J. G. (ed.), The MIT Press, 1990, 235–282.Google Scholar
  2. 2.
    Chan, C. C. and Grzymala-Busse, J. W.: On the attribute redundancy and the learning programs ID3, PRISM, and LEM2. Department of Computer Science, University of Kansas, TR-91-14, 1991, 20 pp.Google Scholar
  3. 3.
    Grzymala-Busse, J. W.: Managing Uncertainty in Expert Systems. Kluwer Academic Publishers, 1991.Google Scholar
  4. 4.
    Grzymala-Busse, J. W.: LERS—A system for learning from examples based on rough sets. In Intelligent Decision Support. Handbook of Applications and Advances of the Rough Sets Theory. Slowinski, R. (ed.), Kluwer Academic Publishers, 1992, 3–18.Google Scholar
  5. 5.
    Grzymala-Busse, J. W.: Managing uncertainty in machine learning from examples. Proc. of the Third Intelligent Information Systems Workshop, Wigry, Poland, June 6–11, 1994, 70–84.Google Scholar
  6. 6.
    Grzymala-Busse, J. W. and Wang, C. P. B.: Classification and rule induction based on rough sets. Proc. of the 5th IEEE International Conference on Fuzzy Systems FUZZ-IEEE'96, New Orleans, Louisiana, September 8–11, 1996, 744–747.Google Scholar
  7. 7.
    Holland, J. H., Holyoak K. J., and Nisbett, R. E.: Induction. Processes of Inference, Learning, and Discovery. The MIT Press, 1986.Google Scholar
  8. 8.
    Michalski, R. S., Mozetic, I., Hong, J. and Lavrac, N. The AQ15 inductive learning system: An overview and experiments. Department of Computer Science, University of Illinois, Rep. UIUCDCD-R-86-1260, 1986.Google Scholar
  9. 9.
    Pawlak, Z.: Rough sets. International Journal Computer and Information Sciences 11, 1982, 341–356.Google Scholar
  10. 10.
    Pawlak, Z.: Rough Sets. Theoretical Aspects of Reasoning about Data. Kluwer Academic Publishers, 1991.Google Scholar
  11. 11.
    Ras, Z. W.: Cooperative query answering. Proc. of the Workshop Intelligent Inf. Syst. IV, Augustow, Poland, June 5–9, 1995, 32–41.Google Scholar
  12. 12.
    Roget's International Thesaurus, Thomas Y. Crowell Company, 1962.Google Scholar
  13. 13.
    Slowinski, R. and Stefanowski, J. Handling various types of uncertainty in the rough set approach. Proc of the RKSD-93, International Workshop on Rough Sets and Knowledge Discovery, 1993, 395–397.Google Scholar
  14. 14.
    Ziarko, W. Analysis of uncertain information in the framework of variable precision rough sets. Found. Computing Decision Sci. 18, 1993, 381–396.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1997

Authors and Affiliations

  • Jerzy W. Grzymala-Busse
    • 1
  • L. John Old
    • 2
  1. 1.University of KansasLawrence
  2. 2.Indiana UniversityBloomington

Personalised recommendations