Corpus Microtext Analysis and English Teaching Model Innovation in Cloud Computing Background

Abstract

The rapid development of cloud computing technology makes the English teaching corpus an excellent choice for innovative English teaching mode. This paper constructs a corpus of English teaching resources and English O2O teaching mode based on MOOC education platform, combines the probabilistic semantic analysis (PLSA) model with the potential semantic indexing to carry out microtext analysis of the English teaching corpus, and solves the PLSA model by using the expectation (EM) algorithm. For the English teaching corpus and English O2O teaching model constructed in this paper, a validation analysis was conducted in three aspects: micro text analysis, text lexical ability and teaching satisfaction. The results show that the PLSA model is able to obtain complete sentence expressions in the microtexture of the corpus when the weight coefficient of the lexical items is set to 0.3. The text vocabulary ability of students in English O2O teaching mode shows a significant difference level at 1% level, and the correlation coefficient of in-class to pre-class reaches 0.889. The English teaching corpus relying on MOOC platform and O2O teaching mode can effectively enhance students’ deep understanding of English sentences, improve students’ satisfaction with English teaching, and enhance students’ interest in learning English.

Keywords

• latent semantic indexing
• plsa model
• corpus
• micro text analysis
• mooc platform
• 00a73