Title: An automatic error correction method for business English text translation based on natural language processing

Authors: Yan Yang

Addresses: Department of Business English, Xi'an Fanyi University, Xi'an 710105, China

Abstract: In order to improve the efficiency and accuracy of traditional translation error-correction methods, this paper proposes an automatic error correction method for business English text translation based on natural language processing. First, the error correction statements are divided into triples by using natural language transformation to determine the logical expression template. Secondly, maximum likelihood is used to estimate word frequency. Then, a naive Bayesian classifier is constructed to classify translation words. Finally, beam search decoding is used to generate target text correction sentences, and an automatic error correction function for text translation is constructed. The results show that the accuracy of translation error correction can reach 99%, the average error of error correction is less than 0.10, and the error correction time is only 4.5 s. This method can improve the accuracy and efficiency of error correction.

Keywords: natural language processing; loss function; business English translation; Markov assumption; naive Bayes.

DOI: 10.1504/IJBIDM.2024.137732

International Journal of Business Intelligence and Data Mining, 2024 Vol.24 No.3/4, pp.218 - 233

Received: 18 Nov 2022
Accepted: 07 Mar 2023
Published online: 04 Apr 2024 *

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