A Study on the Methods of Improving Writing Skills by Bayesian Classifier in English Teaching and Learning

Abstract

Teachers have been researching how to effectively improve the English writing ability of contemporary college students. The article utilizes web crawler technology to establish an English writing knowledge database and preprocesses the data through a word division algorithm, lexical annotation, and word form reduction. The article employs Bayesian classification algorithms to model the English writing style of students. The English writing grammar error correction model was constructed based on the BiGRU model, combined with the BERT pre-training task, fused with the attention mechanism. The English writing knowledge database was used to analyze the data for the model mentioned above. The sentence length of English written text fluctuates between -0.095 and 0.436, and the Bayesian classification model’s AUC value is 0.814. The grammar error correction model for English writing text has a GLEU value of 63.24, with an automatic scoring mean of 26.69, which is only 0.35 higher than manual scoring and a scoring error of only 1.29%. Through the English writing text style and grammar error correction model, teachers can optimize the English writing teaching mode and help students improve their English writing abilities.

Keywords

• web crawler
• participle algorithm
• bayesian classification
• bigru model
• bert pre-training
• english writing ability
• 68t05