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Intelligent Evaluation Algorithm of English Writing Based on Semantic Analysis

Computational intelligence and neuroscience, 2022-10, Vol.2022, p.1-9 [Peer Reviewed Journal]

Copyright © 2022 Jing Wang and Bin Liu. ;Copyright © 2022 Jing Wang and Bin Liu. This is an open access article distributed under the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. https://creativecommons.org/licenses/by/4.0 ;Copyright © 2022 Jing Wang and Bin Liu. 2022 ;ISSN: 1687-5265 ;EISSN: 1687-5273 ;DOI: 10.1155/2022/8955638

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  • Title:
    Intelligent Evaluation Algorithm of English Writing Based on Semantic Analysis
  • Author: Wang, Jing ; Liu, Bin
  • Ye, Jun ; Jun Ye
  • Subjects: Algorithms ; Alliances ; Grammar ; Neural networks ; Neurons ; Semantic analysis ; Semantics ; Speech ; Translations
  • Is Part Of: Computational intelligence and neuroscience, 2022-10, Vol.2022, p.1-9
  • Description: In order to solve the intelligent evaluation of English writing, this paper proposes a method based on the English semantic neural network algorithm. This paper first briefly analyzes the research background of the English semantic analysis system, then expounds on the relevant technologies of the English distance similarity algorithm, semantic analysis intelligent algorithm structure, word analysis algorithm, sentence part of speech analysis algorithm, sentence semantic analysis algorithm, and neural network algorithm, and finally expounds the database and method implementation of the English semantic analysis system, so as to provide guarantee for the design of the English semantic analysis system. The experimental results show that the recognition accuracy of the BRF network for English characters can reach 96.35%, which is 7.79% higher than that of the BP network; the AUC of the BRF network reaches 0.89, which is closer to 1 compared with 0.72 of the BP network. The test results are in good agreement with the antinoise curve test results of the figure. It is proved that the English semantic neural network algorithm can effectively improve the accuracy of English translation and further improve the efficiency of the system.
  • Publisher: New York: Hindawi
  • Language: English
  • Identifier: ISSN: 1687-5265
    EISSN: 1687-5273
    DOI: 10.1155/2022/8955638
  • Source: ProQuest One Psychology
    Geneva Foundation Free Medical Journals at publisher websites
    PubMed Central
    ProQuest Central
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