Differential Analysis of Stylistic Features in English Translation Teaching Based on Semantic Contrastive Analysis
In this paper, in the process of digitization of stylistic features in English translation teaching, the simulated stylistic features in English translation teaching activities are quantified and pre-emphasized to obtain the decoder of stylistic features of English translation with higher precision,...
| Main Author: | |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Sciendo
2024-01-01
|
| Series: | Applied Mathematics and Nonlinear Sciences |
| Subjects: | |
| Online Access: | https://doi.org/10.2478/amns-2024-0085 |
| _version_ | 1797303205523095552 |
|---|---|
| author | Yan Rong |
| author_facet | Yan Rong |
| author_sort | Yan Rong |
| collection | DOAJ |
| description | In this paper, in the process of digitization of stylistic features in English translation teaching, the simulated stylistic features in English translation teaching activities are quantified and pre-emphasized to obtain the decoder of stylistic features of English translation with higher precision, and the stylistic features recognition algorithm in English translation teaching is designed, and the results of the stylistic features recognition in English translation teaching can be obtained by substituting the initial data into the recognition algorithm. Based on stylistic feature recognition, combined with the post-particle swarm optimization algorithm and artificial neural network to construct the stylistic feature analysis model in English translation teaching, and use the method of statistical analysis to analyze the differences of stylistic features in English translation teaching. The results show that the rank means value of auxiliary is the highest, reaching 209.81, the lowest is a preposition (145.17), and the conjunction and adverb are 154.17 and 157.45 respectively, which indicates that auxiliary has the strongest variability of features in the translation of English novels, and this study enables students to have a comprehensive and in-depth understanding of the text, to grasp the stylistic features of the text, and to improve the students’ comprehensive English language ability and translation level. |
| first_indexed | 2024-03-07T23:49:29Z |
| format | Article |
| id | doaj.art-c589e90ebf024bc3b2f1c1f436fdbf42 |
| institution | Directory Open Access Journal |
| issn | 2444-8656 |
| language | English |
| last_indexed | 2024-03-07T23:49:29Z |
| publishDate | 2024-01-01 |
| publisher | Sciendo |
| record_format | Article |
| series | Applied Mathematics and Nonlinear Sciences |
| spelling | doaj.art-c589e90ebf024bc3b2f1c1f436fdbf422024-02-19T09:03:34ZengSciendoApplied Mathematics and Nonlinear Sciences2444-86562024-01-019110.2478/amns-2024-0085Differential Analysis of Stylistic Features in English Translation Teaching Based on Semantic Contrastive AnalysisYan Rong01Nanchong Vocational and Technical College, Nanchong, Sichuan, 637000, China.In this paper, in the process of digitization of stylistic features in English translation teaching, the simulated stylistic features in English translation teaching activities are quantified and pre-emphasized to obtain the decoder of stylistic features of English translation with higher precision, and the stylistic features recognition algorithm in English translation teaching is designed, and the results of the stylistic features recognition in English translation teaching can be obtained by substituting the initial data into the recognition algorithm. Based on stylistic feature recognition, combined with the post-particle swarm optimization algorithm and artificial neural network to construct the stylistic feature analysis model in English translation teaching, and use the method of statistical analysis to analyze the differences of stylistic features in English translation teaching. The results show that the rank means value of auxiliary is the highest, reaching 209.81, the lowest is a preposition (145.17), and the conjunction and adverb are 154.17 and 157.45 respectively, which indicates that auxiliary has the strongest variability of features in the translation of English novels, and this study enables students to have a comprehensive and in-depth understanding of the text, to grasp the stylistic features of the text, and to improve the students’ comprehensive English language ability and translation level.https://doi.org/10.2478/amns-2024-0085particle swarm optimization algorithmartificial neural networkdecodervariability analysisenglish translation teaching00a73 |
| spellingShingle | Yan Rong Differential Analysis of Stylistic Features in English Translation Teaching Based on Semantic Contrastive Analysis Applied Mathematics and Nonlinear Sciences particle swarm optimization algorithm artificial neural network decoder variability analysis english translation teaching 00a73 |
| title | Differential Analysis of Stylistic Features in English Translation Teaching Based on Semantic Contrastive Analysis |
| title_full | Differential Analysis of Stylistic Features in English Translation Teaching Based on Semantic Contrastive Analysis |
| title_fullStr | Differential Analysis of Stylistic Features in English Translation Teaching Based on Semantic Contrastive Analysis |
| title_full_unstemmed | Differential Analysis of Stylistic Features in English Translation Teaching Based on Semantic Contrastive Analysis |
| title_short | Differential Analysis of Stylistic Features in English Translation Teaching Based on Semantic Contrastive Analysis |
| title_sort | differential analysis of stylistic features in english translation teaching based on semantic contrastive analysis |
| topic | particle swarm optimization algorithm artificial neural network decoder variability analysis english translation teaching 00a73 |
| url | https://doi.org/10.2478/amns-2024-0085 |
| work_keys_str_mv | AT yanrong differentialanalysisofstylisticfeaturesinenglishtranslationteachingbasedonsemanticcontrastiveanalysis |