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

<p>Based on the formal definition of semantic language and specific natural language, the research adopts the Word2vec model to map words into a low-dimensional dense space and performs semantic modeling by unsupervised learning through a large amount of unlabeled data to realize the vectorized representation of translated text. For the vectorized text, the text features are combined and filtered based on the TextCNN network, and the TextCNN network is implemented under the Keras framework. Based on the TextCNN network of Keras, the stylistic features of Business English and Daily English are compared at three levels: lexical, syntactic and discourse. Business English was rated 23.6 points higher in terms of conciseness of diction compared to everyday English, and 28.7 points improved the modesty of diction compared to everyday English. This study furthers the improvement of various English translation types.</p>
</abstract>

Based on the formal definition of semantic language and specific natural language, the research adopts the Word2vec model to map words into a low-dimensional dense space and performs semantic modeling by unsupervised learning through a large amount of unlabeled data to realize the vectorized representation of translated text. For the vectorized text, the text features are combined and filtered based on the TextCNN network, and the TextCNN network is implemented under the Keras framework. Based on the TextCNN network of Keras, the stylistic features of Business English and Daily English are compared at three levels: lexical, syntactic and discourse. Business English was rated 23.6 points higher in terms of conciseness of diction compared to everyday English, and 28.7 points improved the modesty of diction compared to everyday English. This study furthers the improvement of various English translation types.

Comparative Analysis of Business English and Everyday English Translation Stylistic Features Based on Keras Neural Networks

Guilin University of Aerospace Technology

Applied Mathematics and Nonlinear Sciences

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

Based on the formal definition of semantic language and specific natural language, the research adopts the Word2vec model to map words into a low-dimensional...

Comparative Analysis of Business English and Everyday English Translation Stylistic Features Based on Keras Neural Networks

Publié en ligne: 08 nov. 2023

Pages: -

Reçu: 11 janv. 2023

Accepté: 19 mai 2023

DOI: https://doi.org/10.2478/amns.2023.2.01041

Mots clés
Keras framework, TextCNN network, Semantic language, Natural language, Translated text, Corpora features

© 2023 Jiawei Xing et al., published by Sciendo

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

Comparative Analysis of Business English and Everyday English Translation Stylistic Features Based on Keras Neural Networks

Publié en ligne: 08 nov. 2023

Pages: -

Reçu: 11 janv. 2023

Accepté: 19 mai 2023

DOI: https://doi.org/10.2478/amns.2023.2.01041

Mots clésKeras framework, TextCNN network, Semantic language, Natural language, Translated text, Corpora features

© 2023 Jiawei Xing et al., published by Sciendo

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

Mots clés
Keras framework, TextCNN network, Semantic language, Natural language, Translated text, Corpora features