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Nowadays, public authorities are actively developing technologies of electronic interaction with organizations and citizens. One of the key tasks in this area is classification of incoming messages for their Программные продукты и системы / Software & Systems 4 (32) 2019 654 operational processing. However, the features of such messages (small size, lack of a clear structure, etc.) do not allow using traditional approaches to the analysis of textual information. To solve this problem, it is proposed to use neural network models (artificial neural networks and neurofuzzy classifier), which allow finding hidden patterns in documents written in a natural language. The choice of a specific method is determined by the approach to forming thematic headings: convolutional neural networks (for unambiguous definition of rubrics); recurrent neural networks (for significant word order in the title of rubrics); neuro-fuzzy classifier (for intersecting thesauri of rubrics).

Особенности применения нейро-сетевых моделей для классификации коротких текстовых сообщений