The Application of Neural Networks in the Construction of a Program for the Markup of Text

Abstract

This paper explores the use and applications of neural networks in the construction of a text markup program. This research paper describes various tasks that require textual data analysis and discusses the problems, issues, and solutions that accompany them. Interest in neural networks has increased in recent years, and they are find­ing applications in a wide variety of fields, such as business, medicine, engineering, geology, and physics. Neural networks have made great strides in forecasting, planning, and management. There are several reasons for this situation. Neural networks are very powerful modeling systems capable of creating complex dependencies. Neural networks can be widely used in areas such as text/speech recognition, semantic search, decision support/expert systems, inventory forecasting, data storage systems and content analysis. The object of the work is the process of functioning of neural networks and an algorithm for text markup recognition. The purpose of the scientific paper is the application of neural networks in the construction of the program for the markup of the text. In order to achieve the goal, the following tasks were put forward: a) study existing neural networks, b) choose a neural network to cre­ate a model and study its structure, c) convert input data to feed it into a neural network model. Representation and analysis of symbolic structures in neural networks seems to be an interesting and useful direction in neural network theory. In this paper, we have reviewed some neural network architectures that may merit further consideration in these circumstances. In what follows, we will focus on specific experiments in this area. Thus, this paper outlines a problem area for further research and testing.