Abstract
A document classification and routing system is described which uses a probabilistic approach to determine the “flavor” of a text. The necessary probabilities are determined from the relevant training documents. Development, refinement, and testing of the system’s ability to route 120,000 documents into 50 topics are discussed as well as the mathematical model on which it is based.
Keywords
- Relevant Document
- Multinomial Distribution
- Document Frequency
- Boolean Expression
- Document Classification
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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© 1999 Springer Science+Business Media Dordrecht
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Guthrie, L., Guthrie, J., Leistensnider, J. (1999). Document Classification and Routing. In: Strzalkowski, T. (eds) Natural Language Information Retrieval. Text, Speech and Language Technology, vol 7. Springer, Dordrecht. https://doi.org/10.1007/978-94-017-2388-6_12
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DOI: https://doi.org/10.1007/978-94-017-2388-6_12
Publisher Name: Springer, Dordrecht
Print ISBN: 978-90-481-5209-4
Online ISBN: 978-94-017-2388-6
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