Application of Natural Language Processing in the Automatic Detection of English Writing Errors

This paper aims to explore a set of automatic detection methods for English writing errors based on natural language processing (NLP) to solve the problem of low error recognition rate in the traditional artificial speech modes. In this paper, an integrated speech acquisition, processing and recognition system is proposed. The system uses language model (LM) to identify errors in English sentences, and realizes accurate translation recognition through neural machine translation (NMT). In addition, BP neural networks are used to fit the English translation process in order to accurately identify English translations. Experimental data show that compared with the traditional artificial speech models, the recognition rate of this model is 0.952, the recall rate is 0.9732, and the F-value is 0.968, which significantly improves the recognition accuracy. The correct recognition rate was 95.96%, and the false positive rate was only 2.22^%. Experimental results show that the proposed method can effectively identify grammatical errors in English sentences, and the system has excellent performance.