Intellectual Information System for Supporting Text Data Rephrasing Processes Based on Deep Learning

Abstract

This paper focuses on the specifics of the an intelligent information system development for supporting the processes of large volumes text data analysis and rephrasing based on deep learning models. An overview of the key problems in the field of content analysis on the Internet, in particular, text data, is carried out, ways of solving existing difficulties are indicated, the artificial neural networks models using necessity to automate the process of natural language processing is substantiated. Paper presents the results of LSTM and Transformer models advantages analysis, software implementation within the framework of an intelligent information system to support dialogue and text paraphrasing functions. System’s design diagrams in UML are presented, its functionality is described and an artificial neural network model’s effectiveness experimental study using the generated test data sets is provided. As a result the paraphrases generation quality metrics were assessed based on the use of the Loss and BLEU indicators. In the conclusion the authors describe ways for the further system’s development and possible researches in related areas of natural language processing based on deep learning.