Fuzzy Semantic Choice in English Machine Translation

Abstract

Fuzzy semantic choice in English machine translation is of great significance for the public to correctly recognize the semantic meaning. At present, the fuzzy semantic selection method in machine translation of English is low in accuracy, while the missed seizure situation in the process of fuzzy semantic selection is more serious. When the large-scale fuzzy semantic selection is made, the search speed of the model is slow. The article analyzes the fuzzy semantic origins of machine translation in English, divides it into two modules: ambiguity in English translation standard and fuzziness in English machine translation program, and studies their internal factors. By Gaussian probability density distribution function The CHMM model in HMM is optimized to construct the fuzzy semantic choice primary model in machine translation of English to achieve the initial improvement of the correctness of semantic selection. The fuzzy semantic selection primitives are extracted, the fuzzy semantic samples are collected, the corpus is constructed to reduce Fuzzy semantic selection in the process of missing inspection; the filler model, as well as the keyword model annotation, according to training to get the important parameters of the HMM; Revaluation of the HMM model revaluation, the English machine translation to get the fuzzy semantic selection of the final Model, and then improve the accuracy of fuzzy semantic selection, and select the speed. Experiments show that the method can achieve high-precision and high-efficiency fuzzy semantic selection, and the missing detection condition is greatly reduced compared with the current method.