Comparative Analysis of Business English and Everyday English Translation Stylistic Features Based on Keras Neural Networks-Reference-Cited by-同舟云学术

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

Published:2023-11-08 Issue:1 Volume:9 Page:
ISSN:2444-8656
Container-title:Applied Mathematics and Nonlinear Sciences
language:en
Short-container-title:

Author:

Xing Jiawei¹,Rong Xiuli¹,Chen Guomin²

Affiliation:

1. 1 School of Foreign Languages and International Business , Guilin University of Aerospace Technology , Guilin , Guangxi , , China .

2. 2 School of Management , Guilin University of Aerospace Technology , Guilin , Guangxi , , China .

Abstract

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.

Publisher

Walter de Gruyter GmbH

Subject

Applied Mathematics,Engineering (miscellaneous),Modeling and Simulation,General Computer Science

Link

https://www.sciendo.com/pdf/10.2478/amns.2023.2.01041

Reference17 articles.

1. Chris, B. (2022). Decolonizing business english: exploring classroom ideologies. ELT Journal(2), 2.

2. Banister, C. (2020). Exploring peer feedback processes and peer feedback meta-dialogues with learners of academic and business english:. Language Teaching Research.

3. Komori-Glatz, M. (2018). Conceptualising english as a business lingua franca. European Journal of International Management, 12(1-2), 46-61.

4. JoséJavier vila-Cabrera a, & B, A. C. E. (2021). The project subespskills: subtitling tasks for students of business english to improve written production skills - sciencedirect. English for Specific Purposes, 63, 33-44.

5. Wang, L., & Fan, J. (2020). Assessing business english writing: the development and validation of a proficiency scale. Assessing Writing, 46(4).