<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>This paper focuses on the differences in grammar rules and syntactic structures between Japanese passive and probable tenses and labels the corresponding sentences in the corpus according to their grammatical features. A statistical method is employed to categorize the corpus by linking dependency trees and morphological information. Differential equation modeling is used to construct a classification model, and the important features and extraction processes for distinguishing sentence structure are defined. In conclusion, the impact of language and cultural differences on the machine translation of Japanese passive sentences is examined. The results show that the significant values of manual translation and machine translation are 0.158 and 0.203, respectively, which are greater than 0.05, indicating that they conform to the normal distribution and there are obvious differences between them. The mean score of the total score of manual translation is 4.62, and the mean score of the total score of machine translation is 2.85, indicating that there is a significant effect of language and cultural differences on machine translation. This paper provides useful guidance and insights for improving the quality of Japanese translation and promoting cross-cultural communication and understanding.</p>
</abstract>

This paper focuses on the differences in grammar rules and syntactic structures between Japanese passive and probable tenses and labels the corresponding sentences in the corpus according to their grammatical features. A statistical method is employed to categorize the corpus by linking dependency trees and morphological information. Differential equation modeling is used to construct a classification model, and the important features and extraction processes for distinguishing sentence structure are defined. In conclusion, the impact of language and cultural differences on the machine translation of Japanese passive sentences is examined. The results show that the significant values of manual translation and machine translation are 0.158 and 0.203, respectively, which are greater than 0.05, indicating that they conform to the normal distribution and there are obvious differences between them. The mean score of the total score of manual translation is 4.62, and the mean score of the total score of machine translation is 2.85, indicating that there is a significant effect of language and cultural differences on machine translation. This paper provides useful guidance and insights for improving the quality of Japanese translation and promoting cross-cultural communication and understanding.

Analysis of Linguistic and Cultural Differences in Japanese Translation Based on Differential Equation Modeling in the Perspective of PMC Assessment

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

This paper focuses on the differences in grammar rules and syntactic structures between Japanese passive and probable tenses and labels the corresponding...

Analysis of Linguistic and Cultural Differences in Japanese Translation Based on Differential Equation Modeling in the Perspective of PMC Assessment

Published Online: Nov 18, 2023

Page range: -

Received: Jan 22, 2023

Accepted: May 29, 2023

DOI: https://doi.org/10.2478/amns.2023.2.01166

Keywords
Differential equation modeling, Japanese being dynamic, Syntactic structure, Corpus, Linguistic and cultural differences

© 2023 Yicai Chen et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Analysis of Linguistic and Cultural Differences in Japanese Translation Based on Differential Equation Modeling in the Perspective of PMC Assessment

Published Online: Nov 18, 2023

Page range: -

Received: Jan 22, 2023

Accepted: May 29, 2023

DOI: https://doi.org/10.2478/amns.2023.2.01166

KeywordsDifferential equation modeling, Japanese being dynamic, Syntactic structure, Corpus, Linguistic and cultural differences

© 2023 Yicai Chen et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Keywords
Differential equation modeling, Japanese being dynamic, Syntactic structure, Corpus, Linguistic and cultural differences