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Method of text classification without the use of training
https://doi.org/10.17586/0021-3454-2026-69-1-90-94
Abstract
A new approach to text classification is proposed that does not employ machine learning methods or require a training set. The method is based on the Damerau-Levenshtein distance, which is the minimum number of editing operations required to transform one string into another and takes into account the semantic similarity of words, weighting of editing operations, and the order of importance of words. The main metrics for assessing the quality of a text classifier and the results of testing the proposed method against these metrics are presented.
About the Authors
T. M. Tatarnikova
St. Peterdburg State University of Aerospace Instrumentation
Russian Federation
Russian Federation
Tatyana M. Tatarnikova — Dr. Sci., Professor; Institute of Information Technologies and Programming; Director of the Institute
St. Petersburg
D. R. Milyaev
St. Petersburg Electrotechnical University „LETI“
Russian Federation
Russian Federation
Dmitry R. Milyaev — Post-Graduate Student; Department of Information Systems
St. Petersburg
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Review
For citations:
Tatarnikova T.M., Milyaev D.R. Method of text classification without the use of training. Journal of Instrument Engineering. 2026;69(1):90-94. (In Russ.) https://doi.org/10.17586/0021-3454-2026-69-1-90-94
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