The paper is devoted to the creation of methods for automatic assessment of the consistency of scientific and technological development priorities and the passports of science majors of the Higher Attestation Commission. The paper proposes a multi-step approach, including the construction of collections of topically similar documents for the priorities and the passports, assessment of the proximity of the found documents using cross-language neural network encoders, and formation of a categorical assessment of the consistency of priorities and passports based on the obtained indicators of document proximity. Experimental studies on a specially created corpus confirm the high quality of the results obtained with the proposed method.
Math-Net.Ru: https://www.mathnet.ru/eng/iipr665
Devyatkin D. A., Tikhomirov I. A., Shkereda D. N., Sochenkov I. V., Grigoriev O. G. Methods of short text analysis for comparing priorities of scientific and technological development with descriptions of science majors // Artificial Intelligence and Decision Making, 2026, Issue1, pp. 137–151.