Research on Text Error Correction Algorithm Design in English Writing Aid System for Non-Native Speakers

This paper studies the design of text error correction algorithm in the English writing aid system for nonnative speakers based on neural network algorithm, aiming at improving the English writing level and text quality of nonnative speakers. First, this paper analyzes the common types of mistakes made by non-native speakers in English writing, including vocabulary, grammar, spelling and punctuation errors. Based on these error types, this paper designs a text error correction algorithm based on neural network, which combines deep learning technology and natural language processing method. The algorithm uses models such as convolutional neural network (CNN) and recurrent neural network to detect and correct text errors at multiple levels. Through the training of a large number of labeled data, the model can effectively capture the complex structure and semantic relations in the language, and achieve high-precision error recognition and correction. This paper uses a large corpus of English learners to carry out experiments, and the results show that the algorithm based on neural network is significantly better than the traditional rule-based and statistical methods in terms of error recognition and correction accuracy and recall rate.