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Intelligent Recognition Model of Business English Translation Based on Improved GLR Algorithm

Aiming at the problem of low accuracy of traditional algorithm model, an intelligent recognition model of business English translation based on an improved GLR algorithm is proposed. Through this algorithm, the automatic sentence recognition technology is established, and according to the characteri...

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Detalles Bibliográficos
Autores principales: Deng, Laiying, Hu, Xinde, Liu, Fen
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9033330/
https://www.ncbi.nlm.nih.gov/pubmed/35463231
http://dx.doi.org/10.1155/2022/4105942
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author Deng, Laiying
Hu, Xinde
Liu, Fen
author_facet Deng, Laiying
Hu, Xinde
Liu, Fen
author_sort Deng, Laiying
collection PubMed
description Aiming at the problem of low accuracy of traditional algorithm model, an intelligent recognition model of business English translation based on an improved GLR algorithm is proposed. Through this algorithm, the automatic sentence recognition technology is established, and according to the characteristics of business English, the improved GLR algorithm is used for collection, sorting, and analysis, so as to realize the intelligent recognition of business English. The results show that based on the improved GLR algorithm, the recognition accuracy is high, and the comprehensive score is 92.5 points, which overcomes the disadvantages of the GLR algorithm, and the operation speed and processing are improved. Based on the improved GLR algorithm, the intelligent translation of business English is realized, which is accurate and fast, and greatly promotes the learning and development of business English.
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spelling pubmed-90333302022-04-23 Intelligent Recognition Model of Business English Translation Based on Improved GLR Algorithm Deng, Laiying Hu, Xinde Liu, Fen Comput Intell Neurosci Research Article Aiming at the problem of low accuracy of traditional algorithm model, an intelligent recognition model of business English translation based on an improved GLR algorithm is proposed. Through this algorithm, the automatic sentence recognition technology is established, and according to the characteristics of business English, the improved GLR algorithm is used for collection, sorting, and analysis, so as to realize the intelligent recognition of business English. The results show that based on the improved GLR algorithm, the recognition accuracy is high, and the comprehensive score is 92.5 points, which overcomes the disadvantages of the GLR algorithm, and the operation speed and processing are improved. Based on the improved GLR algorithm, the intelligent translation of business English is realized, which is accurate and fast, and greatly promotes the learning and development of business English. Hindawi 2022-04-15 /pmc/articles/PMC9033330/ /pubmed/35463231 http://dx.doi.org/10.1155/2022/4105942 Text en Copyright © 2022 Laiying Deng et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Deng, Laiying
Hu, Xinde
Liu, Fen
Intelligent Recognition Model of Business English Translation Based on Improved GLR Algorithm
title Intelligent Recognition Model of Business English Translation Based on Improved GLR Algorithm
title_full Intelligent Recognition Model of Business English Translation Based on Improved GLR Algorithm
title_fullStr Intelligent Recognition Model of Business English Translation Based on Improved GLR Algorithm
title_full_unstemmed Intelligent Recognition Model of Business English Translation Based on Improved GLR Algorithm
title_short Intelligent Recognition Model of Business English Translation Based on Improved GLR Algorithm
title_sort intelligent recognition model of business english translation based on improved glr algorithm
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9033330/
https://www.ncbi.nlm.nih.gov/pubmed/35463231
http://dx.doi.org/10.1155/2022/4105942
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