Analyzing the Application of Multimedia Technology Assisted English Grammar Teaching in Colleges

Abstract

To address the problem that the traditional English composition is manually reviewed, which leads to low efficiency of grammar error checking and heavy teaching burden for teachers and seriously affects the quality of English teaching, a verb grammar error checking algorithm based on the combination of a multilayer rule model and a deep learning model is proposed with current multimedia technology. First, the basic principles and network structure of the long short-term memory network LSTM are specifically analyzed; then, a multilayer rule model is constructed; after that, an attention model-based LSTM verb grammar checker is designed, and a word embedding model is used to encode the text and map it to the status space to retain the textual information. Finally, the proposed algorithm is experimentally validated using the corpus dataset. The experimental results revealed that the accuracy, recall, and F1 values of the proposed algorithm in verb error detection were 45.51%, 28.77%, and 35.5%, respectively, which were higher than those of the traditional automatic review algorithm, indicating that the proposed algorithm has superior performance and can improve the accuracy and efficiency of grammar error detection.