G.S. Ivanova1, P.A. Martynyuk2
1, 2 Bauman Moscow State Technical University (Moscow, Russia)

1 gsivanova@bmstu.ru, 2 martynyuk.pa@bmstu.ru

In the conditions of the modern information society, the volume of text information is constantly increasing, creating the need to develop and implement effective methods for processing it and extracting useful information. Automating this process becomes crucial to improving the speed and accuracy of text data processing. Current approaches include rule-based methods, as well as more recent methods using machine and deep learning algorithms. Despite their diversity and effectiveness, each of these approaches has its own limitations and applications. This article is devoted to the analysis of classes of information extraction methods and the survey of real text data processing systems that implement these methods in practice.

The purpose of this work is to carry out a study of the main tasks and methods of extracting information from texts used in text document analysis systems, as well as to analyze existing systems that implement these methods and their combinations. The analysis aims to identify the advantages and limitations of methods based on data from real software systems.

In the course of the work, the main tasks of information extraction (extraction of named entities and extraction of relationships) have been considered and also classes of information extraction methods that implement these tasks: rules-based methods; methods based on machine learning; methods based on deep learning. The advantages and disadvantages of each method have been revealed. The article also discusses examples of real systems, both implementing methods of one class and using combinations of methods from different classes – hybrid systems. As a result of the analysis performed, the article identifies a number of main problems in the subject area of analyzing unstructured text documents, and also suggests ways to overcome the vulnerabilities of certain classes of methods for extracting information from text.

The research has practical value for developers of text data processing systems and analysts working with large volumes of information. The information presented in the work about various approaches to extracting information from text allows specialists to get a clear understanding of each of them, as well as evaluate the prospects for using methods using examples of real text data processing systems. In addition, with the rapid development of technology and increasing data volumes, this study provides relevant information to help adaptation of existing systems and processes to modern requirements and challenges.

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