Research on Efficient English Teaching Model Combining Corpus Microtext Analysis

Abstract

Aiming at the goal of improving the quality of English teaching and combining the characteristics of English teaching, this paper constructs an efficient English discursive literacy teaching based on corpus micro-text analysis. Utilizing the unique advantages of online corpus to assist English teaching, it proposes the targeted teaching of English metaphorical vocabulary and combines the Word2Vec word embedding model and the cosine similarity algorithm to propose a model for analyzing the quality of English text coherence. Based on the characteristics of writing vocabulary in microtexts of the corpus, the analysis integrates the dimensional features of English writing vocabulary teaching. The effectiveness of the corpus-based efficient teaching model for English literacy teaching is verified through teaching practice, specifically utilizing the average values of pre- and post-tested vocabulary, sentences, and chapters of the two classes as comparison data. The results proved that the average value of the pre-test scores of all students in the experimental class is 7.78, and the average value of the post-test is 10.72, which is 2.94 higher than the pre-test. The English teaching model in colleges and universities based on corpus micro-text analysis can significantly improve the English writing level of students and achieve efficient English teaching.

Keywords

• corpus
• word2vec word embedding
• cosine similarity
• efficient english teaching
• 00a73