Simulation of E-learning in English learner writing teaching based on natural language processing and machine learning algorithms

Abstract

This article collected a large number of writing samples from computer English learners, which included various types of writing errors. Subsequently, natural language processing techniques were used to extract and preprocess the features of these writing samples. Select the decision tree algorithm as the construction method for the error classification model, and classify the samples by constructing a tree like structure. In the process of building the model, writing samples are used as training sets, feature values are used to determine the type of writing errors, and they are classified. Based on the classification results of the model for samples, corresponding correction suggestions and improvement strategies are provided. A fault classification model based on natural language processing and decision tree algorithm is constructed, and corresponding correction suggestions and improvement strategies are provided. This model has shown good results in experiments and evaluations, and can accurately identify and correct various common writing errors. The research results indicate that the method is effective in helping computer English learners improve their writing ability and accuracy. Future research will further explore other machine learning algorithms and technologies to further improve the effectiveness of error classification and correction.