Neurocomputer System of Semantic Analysis of the Text in the Kazakh Language

Abstract

The purpose of the study is to solve an extreme mathematical problem—semantic analysis of natural language, which can be used in various fields, including marketing research, online translators, and search engines. When training the neural network, data training methods based on the latent Dirichlet allocation model and vector representation of words were used. This study presents the development of a neurocomputer system used for the purpose of semantic analysis of the text in the Kazakh language, based on machine learning and the use of the latent Dirichlet allocation model. In the course of the study, the stages of system development were considered, regarding the text recognition algorithm. The Python programming language was used as a tool using libraries that greatly simplify the process of creating neural networks, including the Keras library. An experiment was conducted with the involvement of experts to test the effectiveness of the system, the results of which confirmed the reliability of the data provided by the system. The papers of modern computer linguists dealing with the problems of natural language processing using various technologies and methods are considered.