Research on Text Analysis Methods in English Translation Teaching Based on Semantic Association Network Modeling

As the mainstream language of the world, English still occupies an unshakeable prime position, and English translation is one of the essential learning content for students. The semantic network association network model is introduced in this paper, and a text analysis model for English translation is proposed. The model obtains word vectors through the GloVe word embedding model, combines the relevance measure of the Jaccard index, and applies the improved RelArtNet algorithm to calculate semantic relevance in English texts. The translated data from a prominent English test is selected as a sample to analyze the relevance of each feature value of English translation scores. From the three aspects of word surface, conceptual fidelity, and semantic expression, the textual analysis of the experimental corpus by the model yields the comprehensive scoring effect of English translation by the model. The correlation between translation length and English translation scoring is the highest, and the correlation of scoring in both English to Chinese and Chinese to English is more significant than 0.58. The overall model scoring and manual scoring of English translation are closer, with a difference of 1.59 in scoring and a correlation coefficient of greater than 0.8, which verifies the practical value of the model for scoring English translations. The English translation scoring model can help teachers review translations and students practice independently, which has important practical significance.