МУЛЬТИМОДЕЛЬНЫЙ ПОДХОД К ИНТЕЛЛЕКТУАЛЬНОМУ АНАЛИЗУ ОБРАЩЕНИЙ ГРАЖДАН

Abstract

Today, implementation of the concept of electronic government is one of the priority tasks of state policy in Russia. One of the elements of this concept is organizing effective interaction between authorities and citizens (the Government-to-Citizen model), which, besides providing public services, should include processing of electronic applications (applications, complaints, suggestions, etc.). In turn, the speed and efficiency of processing the incoming requests depends to a large extent on the quality of the definition of the corresponding thematic heading, i.e. solving the problem of rubrication (classification). An analysis of citizens’ appeals to e-mail and official websites of various government bodies revealed a number of specific features (small size, errors in the text, free style of presentation, description of several problems) that do not allow the successful application of traditional approaches to rubrication. To solve this problem, it was proposed to use various methods of mining unstructured text data (in particular, fuzzy-logical algorithms, fuzzy decision trees, fuzzy pyramidal networks, neuro-fuzzy classifier, convolutional and recurrent neural networks). This article describes a new approach to the analysis of electronic communications from citizens, based on the complex application of several rubrication models, which is distinguished by taking into account the degree of intersection of thematic headings, the dynamism of their thesauri and the volume of accumulated statistical information. For a situation where a specific model cannot make an unambiguous choice of a thematic heading, it is proposed to use the method of voting of classifiers, which can significantly reduce the probability of classification errors based on weighted aggregation of solutions obtained by several models.