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

<p>In this paper, the support vector machine is used as the overall classification of the paragraph alignment method. The paragraph length relationship is taken as an essential feature of the business English paragraph alignment model, the CRFs discriminative model is used as the word alignment task model, and the maximum likelihood estimation algorithm is used for model parameter training. Aiming at the real-time alignment needs at the sentence or paragraph level in Business English, the word alignment algorithm based on semantic similarity calculation is proposed, and the evaluation indexes of English-Chinese EC, Chinese-English CE unidirectional alignment and bi-directional fusion alignment, and semantic extension alignment are compared. Adding traditional business English translation techniques, comparing the effects of conventional translation techniques on business English sentence and paragraph translation versus parallel corpus-based translation techniques. The dynamic chunking accuracy of two-way fusion alignment and semantic expansion alignment is 94.57% and 94.32%, respectively, and the accuracy of semantic expansion alignment is 6.85% and 8.17% higher than that of EC one-way alignment. Meanwhile, the number of errors in business English translation works based on parallel corpora is reduced, which can help translators overcome the interference of Chinese chapter structures and improve the quality of their translation works.</p>
</abstract>

In this paper, the support vector machine is used as the overall classification of the paragraph alignment method. The paragraph length relationship is taken as an essential feature of the business English paragraph alignment model, the CRFs discriminative model is used as the word alignment task model, and the maximum likelihood estimation algorithm is used for model parameter training. Aiming at the real-time alignment needs at the sentence or paragraph level in Business English, the word alignment algorithm based on semantic similarity calculation is proposed, and the evaluation indexes of English-Chinese EC, Chinese-English CE unidirectional alignment and bi-directional fusion alignment, and semantic extension alignment are compared. Adding traditional business English translation techniques, comparing the effects of conventional translation techniques on business English sentence and paragraph translation versus parallel corpus-based translation techniques. The dynamic chunking accuracy of two-way fusion alignment and semantic expansion alignment is 94.57% and 94.32%, respectively, and the accuracy of semantic expansion alignment is 6.85% and 8.17% higher than that of EC one-way alignment. Meanwhile, the number of errors in business English translation works based on parallel corpora is reduced, which can help translators overcome the interference of Chinese chapter structures and improve the quality of their translation works.

A Study of Business English Translation Skills Based on Parallel Corpus

Xiangsihu College of Guangxi Minzu University

Applied Mathematics and Nonlinear Sciences

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

{"article-title":"A Study of Business English Translation Skills Based on Parallel Corpus"}

In this paper, the support vector machine is used as the overall classification of the paragraph alignment method. The paragraph length relationship is taken...

A Study of Business English Translation Skills Based on Parallel Corpus

Online veröffentlicht: 15. Mai 2024

Seitenbereich: -

Eingereicht: 22. Jan. 2024

Akzeptiert: 31. März 2024

DOI: https://doi.org/10.2478/amns-2024-1121

Schlüsselwörter
Support vector machine, CRFs, Semantic similarity, Word alignment algorithm, Business English

© 2024 Wanni Mo, et al., published by Sciendo

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

A Study of Business English Translation Skills Based on Parallel Corpus

Online veröffentlicht: 15. Mai 2024

Seitenbereich: -

Eingereicht: 22. Jan. 2024

Akzeptiert: 31. März 2024

DOI: https://doi.org/10.2478/amns-2024-1121

SchlüsselwörterSupport vector machine, CRFs, Semantic similarity, Word alignment algorithm, Business English

© 2024 Wanni Mo, et al., published by Sciendo

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

Schlüsselwörter
Support vector machine, CRFs, Semantic similarity, Word alignment algorithm, Business English