In order to solve the problems of low correction accuracy and long correction time in the traditional English grammar error correction system, an English grammar error correction system based on deep learning is designed in this paper. This method analyzes the business requirements and functions of the English grammar error correction system and then designs the overall architecture of the system according to the analysis results, including English grammar error correction module, service access module, and feedback filtering module. The multilayer feedforward neural network is used to construct the language model to judge whether the language sequence is a normal sentence, so as to complete the correction of English grammatical errors. The experimental results show that the designed system has high accuracy and fast speed in correcting English grammatical errors.
To address the problem of low kappa, precision and recall values, and high misjudgment rate in traditional methods, this study proposes an English grammatical error identification method based on a machine translation model. For this purpose, a bidirectional long short-term memory (Bi-LSTM) model is established to diagnose English grammatical errors. A machine learning (ML) model, i.e., Naive Bayes is used for the result classification of the English grammatical error diagnosis, and the N-gram model is utilized to effectively point out the location of the error. According to the preprocessing results, a grammatical error generation model is designed, a parallel corpus is built from which a training dataset for the model training is generated, and different types of grammatical errors are also checked. The overall architecture of the machine translation model is given, and the model parameters are trained on a large-scale modification of the wrong learner corpus, which greatly improves the accuracy of grammatical error identification. The experimental outcomes reveal that the model used in this study significantly improves the kappa value, the precision and recall values, and the misjudgment rate remains below 1.0, which clearly demonstrates that the detection effect is superior.
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