Intelligent Recognition Model of Business English Translation Based on Improved GLR Algorithm

Deng, Laiying; Hu, Xin; Liu, Fen

doi:10.1155/2022/4105942

Cited by 6 publications

(5 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…An improved GLR method [15] was provided to address the issue of data point unpredictability in the traditional GLR technique. The term arrangement according to the enhanced algorithm was created by applying the term's central theme for analysis the semantic function of the proportional table, accurate the inconsistency of the English Chinese and portions of recognizing speech constructions, overcome the issue of imperfection in identifying outcomes in conventional processing methods and offer an efficient technique for phrase recognition.…”

Section: Related Workmentioning

confidence: 99%

An Intelligent Evaluation Method of Business English Translation Ability Based on Collaborative Game Particle Swarm Algorithm

Yu,

Zhou,

Zhang

2024

Preprint

View full text Add to dashboard Cite

In today's globalized business climate, translation is essential for promoting successful communication. For precise and dependable communication, it is crucial to assess the translation skills of experts in the area of business English. However, the current evaluation techniques frequently lack clever and team-based methodologies to evaluate translation talents completely. In order to fill this gap, the Collaborative Game Particle Swarm Algorithm (CGPSA) is used in this study to present an intelligent evaluation approach for Business English translation proficiency. The CGPSA develops an interesting and efficient evaluation procedure by fusing the ideas of game theory and particle swarm optimization (PSO). The development of computerized sentence recognition that is specifically adapted to the features of business English has been greatly aided by the CGPSA. The CGPSA has been used to collect, organize, and analyze data, enabling sophisticated identification of business English. With a total score of 93.4, the suggested framework's results show high recognition accuracy. The findings show that in terms of accuracy in recognition, frequency magnitude, and intelligent translation, the CGPSA-based assessment technique performs better than conventional evaluation methodologies.

show abstract

Section: Related Workmentioning

confidence: 99%

An Intelligent Evaluation Method of Business English Translation Ability Based on Collaborative Game Particle Swarm Algorithm

Yu,

Zhou,

Zhang

2024

Preprint

View full text Add to dashboard Cite

show abstract

“…We will identify the gaps in the current research and explain how our proposed approach addresses these limitations. The [4] proposes an intelligent recognition model for business English translation based on an improved GLR algorithm. The results show a high recognition accuracy of 92.5 points, overcoming the limitations of traditional algorithms and significantly improving operation speed and processing.…”

Section: Related Workmentioning

confidence: 99%

Research on Automatic Identification of Machine English Translation Errors Based on Improved GLR Algorithm

2024

IJCAI

View full text Add to dashboard Cite

Machine translation is a powerful tool for overcoming linguistic obstacles, but it often introduces errors that lower the overall translation quality. This research project aims to enhance machine-translated documents by identifying and classifying translation faults. To identify errors, the traditional Generalized LR (GLR) technique is modified and enhanced, incorporating linguistic and statistical elements from the machine-translated texts. Contextual information from GLR parsing is utilized to improve error detection, and additional parsing algorithms are integrated to handle the complexities of machine translation. The proposed improved GLR algorithm is compared with three baseline models: the statistical algorithm, dynamic memory algorithm, and traditional GLR algorithm. The evaluation is based on two key metrics: accuracy and recognition speed, with a focus on renewal capability. The improved GLR algorithm achieves a significantly higher accuracy of 92.5% compared to the baseline models: statistical algorithm (85.2%), dynamic memory algorithm (88.9%), and traditional GLR algorithm (80.6%). Additionally, the improved GLR algorithm demonstrates a recognition speed of 1200 words per second, showcasing its efficiency in real-time translation scenarios. The results show that the enhanced GLR algorithm outperforms the baseline models in accurately detecting translation errors while maintaining an efficient recognition speed. Its high renewal capability ensures adaptability to changing translation challenges and continuous improvement over time.Povzetek: Raziskava izboljša avtomatsko identifikacijo napak v strojnem prevajanju z nadgrajenim GLR algoritmom, dosegajoč 92.5% natančnost in hitrost 1200 besed na sekundo.

show abstract

“…To fulfill the design of the English translation model, the recognized content is finally collected. However, the model struggles to correctly detect the part of speech of English phrases, which harms the quality of the ensuing English translation [31]. Interactive information retrieval issues may be solved using ontology-based techniques for semantic document recognition and representation.…”

Section: Related Workmentioning

confidence: 99%

Feature Extraction of English Semantic Translation Relying on Graph Regular Knowledge Recognition Algorithm

Yang

2023

IJCAI

View full text Add to dashboard Cite

Under the background of big data, people are not only pursuing the quantity but also the accuracy of knowledge in acquiring knowledge, especially for English. Because of the ambiguity, variety, and irregularity of English translation, people's reading has brought a lot of trouble. This paper aims to study the feature extraction of English semantic translation and suggests a recognition algorithm that relies on graph common knowledge. Through the analysis of graph regularization and the construction of the model, the recognition algorithm is improved, and the feature extraction methods are compared and analyzed. At the same time, experiments are intended to investigate the improvement of the English semantic translation of the improved recognition algorithm after feature extraction. The experimental results in this paper show that the improved English semantic translation has increased by 10%-15% in terms of translation accuracy. This degree of improvement has great application significance in actual English semantic translation.Povzetek: Opisana je raziskava izboljšav angleškega semantičnega prevoda z uporabo izboljšanega algoritma prepoznavanja značilnosti.

show abstract

Intelligent Recognition Model of Business English Translation Based on Improved GLR Algorithm

Cited by 6 publications

References 17 publications

An Intelligent Evaluation Method of Business English Translation Ability Based on Collaborative Game Particle Swarm Algorithm

An Intelligent Evaluation Method of Business English Translation Ability Based on Collaborative Game Particle Swarm Algorithm

Research on Automatic Identification of Machine English Translation Errors Based on Improved GLR Algorithm

Feature Extraction of English Semantic Translation Relying on Graph Regular Knowledge Recognition Algorithm

Contact Info

Product

Resources

About