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

<p>Language genre is a form of language expression formed by certain people from specific purposes in a particular context, and no form of language expression can exist independently of genre characteristics. The study adopts the space vector model to digitally represent the potential semantics of business English translation. By constructing a translation-word matrix and applying the TF-IDF method to calculate the weights of the elements in the matrix, the study proceeded to perform a singular value decomposition of the matrix. This process helps to extract the features of corpora similarity in business English translation, which in turn completes the construction of a language model for business English translation. The study selected a business English translation corpus for instance analysis. The data results show that in the interaction and information feature analysis, the mean values of ECQ (interaction feature quantitative value) features are higher than TCQ (information feature quantitative value) in three different periods, with the differences of 0.0992, 0.0534 and 0.043 respectively. Regarding the standardized scores of the stylistic features, 24 stylistic feature indexes meet the standardization requirements, accounting for 75% of the total indexes. This finding provides an essential basis for understanding the differences between Business English and other stylistic features. The research in this paper has significant application value for professionals in business English translation.</p>
</abstract>

Language genre is a form of language expression formed by certain people from specific purposes in a particular context, and no form of language expression can exist independently of genre characteristics. The study adopts the space vector model to digitally represent the potential semantics of business English translation. By constructing a translation-word matrix and applying the TF-IDF method to calculate the weights of the elements in the matrix, the study proceeded to perform a singular value decomposition of the matrix. This process helps to extract the features of corpora similarity in business English translation, which in turn completes the construction of a language model for business English translation. The study selected a business English translation corpus for instance analysis. The data results show that in the interaction and information feature analysis, the mean values of ECQ (interaction feature quantitative value) features are higher than TCQ (information feature quantitative value) in three different periods, with the differences of 0.0992, 0.0534 and 0.043 respectively. Regarding the standardized scores of the stylistic features, 24 stylistic feature indexes meet the standardization requirements, accounting for 75% of the total indexes. This finding provides an essential basis for understanding the differences between Business English and other stylistic features. The research in this paper has significant application value for professionals in business English translation.

Characterization of Business English Translation Styles Based on Semantic Contrastive Analysis

Applied Mathematics and Nonlinear Sciences

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

Language genre is a form of language expression formed by certain people from specific purposes in a particular context, and no form of language expression can...

Characterization of Business English Translation Styles Based on Semantic Contrastive Analysis

Publicado en línea: 26 feb 2024

Páginas: -

Recibido: 15 ene 2024

Aceptado: 22 ene 2024

DOI: https://doi.org/10.2478/amns-2024-0577

Palabras clave
Spatial vector model, TF-IDF, Singular values, Similarity features, Stylistic features

© 2024 Xiangyan Ji, published by Sciendo

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

Characterization of Business English Translation Styles Based on Semantic Contrastive Analysis

Publicado en línea: 26 feb 2024

Páginas: -

Recibido: 15 ene 2024

Aceptado: 22 ene 2024

DOI: https://doi.org/10.2478/amns-2024-0577

Palabras claveSpatial vector model, TF-IDF, Singular values, Similarity features, Stylistic features

© 2024 Xiangyan Ji, published by Sciendo

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

Palabras clave
Spatial vector model, TF-IDF, Singular values, Similarity features, Stylistic features