Inference of Discourse Hierarchical Features in English Corpus Based on Multilayer Bayesian Modeling

This paper introduces a multilayer Bayesian model based on probabilistic and Bayesian inference models to infer discourse hierarchical features in the English corpus at both intra-individual and inter-individual behavior levels. Based on the existing English corpus observation data, the Bayesian method is used to organically combine the prior knowledge and the observed English corpus discourse hierarchical features data, derive and incorporate the posterior probability distribution of many uncertain information target variables, and obtain the discourse hierarchical features of excellent English teachers in the English corpus, which provides more valuable information for educational management. For the emotional features of different teachers’ classroom discourse, the level of “emotionally full” is higher than that of “emotionally depressed”, which indicates that the classroom emotions of excellent teachers are more full, demonstrating the discourse hierarchy features of excellent English teachers. The Bayesian estimation has excellent estimation accuracy and explains the discourse hierarchy of teachers in the English corpus well.