НЕЙРОМЕРЕЖ ЕВА ТЕХНОЛОГІЯ ВИЯВЛЕННЯ ПОМИЛОК У ТЕКСТОВИХ ДОКУМЕНТАХ

Serhii Udovenko; Volodymyr Zatkhey; Oleg Teslenko; Serhii Udovenko; Volodymyr Zatkhey; Oleg Teslenko

doi:10.30888/2663-5712.2025-34-01-115

Authors

Serhii Udovenko Simon Kuznets Kharkiv National University of Economics https://orcid.org/0000-0001-5945-8647
Volodymyr Zatkhey Simon Kuznets Kharkiv National University of Economics https://orcid.org/0000-0003-4426-7789
Oleg Teslenko Simon Kuznets Kharkiv National University of Economics https://orcid.org/0000-0003-3105-9323

DOI:

https://doi.org/10.30888/2663-5712.2025-34-01-115

Keywords:

electronic text analysis, error detection in text documents, neural network modeling, principal component method, autoassociative three-layer perceptron.

Abstract

The goal of this work is to develop a modified technology for error detection in text documents using a multilayer perceptron neural network. The proposed technology is implemented using an autoassociative neural network trained on a corpus of parallel te

References

Susanto R., Phandi P. System combination for grammatical error correction / In Proc. of EMNLP, 2014. – P. 21–33.

Chala L., Udovenko S., Hrynev S. Method of neural network correction of errors in editable electronic texts. Bionics of intelligence, 2017. – Issue 1 (88). – P. 15-21.

Zhang F. Design and application of an automatic scoring system for english composition based on artificial intelligence Technology. Int. J. Adv. Comput. Sci. , 2023. 14(8). – Р. 195–205.

Kalchbrenner, N., Blunsom P. Recurrent Continuous Translation Models. In Proceedings of the Conference on Empirical Methods in Natural Language Processing. 2013, P. 1700 – 1709.

NEURAL NETWORK TECHNOLOGY FOR DETECTING ERRORS IN TEXT DOCUMENTS

Authors

DOI:

Keywords:

Abstract

References

Downloads

Published

How to Cite

Issue

Section

License

Most read articles by the same author(s)

Language