Automatic proofreading method for English translation accuracy of nano vocabulary

Liu, Yamei

doi:10.1007/s41204-021-00145-w

Cited by 4 publications

(1 citation statement)

References 16 publications

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“…Let λ be the optimal context of semantic context relevance matrix S w T S B and f be the standard to measure the relevance of semantic context, then the value of φ can directly reflect the mapping of relevance process [26]. The semantic context correlation matrix S w T S B has at most L − 1 optimal translation contexts, and the extracted optimal contexts are RðR ≤ L − 1Þ; then, the characteristic semantics in the optimal context can be expressed by β:…”

Section: Contextual Feature Extractionmentioning

confidence: 99%

English Translation Model Based on Intelligent Recognition and Deep Learning

2022

Wireless Communications and Mobile Computing

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Aiming at the problems of low accuracy of English phrase part of speech recognition, poor English translation effect, and long translation time in the traditional English translation model, an English translation model based on intelligent recognition and deep learning is designed. An English phrase corpus was built, the phrase antecedent and postscript likelihood of the improved GLR algorithm by using the quaternion cluster were calculated, and the part of speech of the English phrase corpus was identified. According to the recognition results, the feature extraction algorithm is introduced to extract the best contextual features. On this basis, a neural machine translation model is constructed by integrating the traditional neural network in deep learning and combining the attention mechanism. It is used as a neural machine translation model for English translation. The simulation results show that the English translation model based on intelligent recognition and deep learning has high phrase recognition accuracy, good translation effect, and short translation time, which improves the quality of English translation.

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Section: Contextual Feature Extractionmentioning

confidence: 99%