Harold Borko
Abstract
This study reports the results of a series of experiments in the techniques of automatic document classification. Two different classification schedules are compared along with two methods of automatically classifying documents into categories. It is concluded that, while there is no significant difference in the predictive efficiency between the Bayesian and the Factor Score methods, automatic document classification is enhanced by the use of a factor-analytically-derived classification schedule. Approximately 55 percent of the document were automatically and correctly classified.
- 1 Institute Radio Engineers, 1959. Abstracts of current computer literature. IRE Trans. EC-8, 1, 2, and 3.Google Scholar
- 2 BORKO, I-I. The construction of an empirically based mathematically derived classification system. Proc. Spring Joint Comput. Conf. 21 (1962), 279-289.Google Scholar
- 3 BORKO, H., AND BERNICK, M. Automatic document classification. J. ACM, 10 (1963), 151-102. Google Scholar
- 4 FRUCHTER, B., AND JENNINGS, E. Factor analysis no. 1. In H. BORKO (Ed.), Computer Applications in the Behavioral Sciences, Prentice-Hall, Englewood Cliffs, N. J., 1962.Google Scholar
- 5 HARMAN, H. H. Modern Factor Analysis. U. of Chicago Press, Chicago, Ill., 1960.Google Scholar
- 6 LUHN, H. F. A statistical approach to mechanized encoding and searching of literary information. IBM J. Res. Develop. i (1957), 309-317.Google Scholar
- 7 MARON, M. E. Automatic indexing: an experimental inquiry. J. ACM 8 (1961), 407-4t7. Google Scholar
- 8 OLNAY, J. C. FEAT, an inventory program for information retrieval. FN-4018, System Development Corp., Santa Moniea, Calif., 1960.Google Scholar
Index Terms
- Automatic Document Classification Part II . Additional Experiments
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