Publicado en 3C Tecnología – Volume 12 Issue 1 (Ed. 43)
To ensure the normal operation of English composition grammar correction and avoid inaccurate detection caused by faults, it is of great significance to detect abnormal working conditions in time and diagnose them accurately. Aiming at the complexity of grammar correction, this paper proposes a PLSTM-CNN model for fault detection in the grammar correction process. The model effectively combines the global feature extraction ability of LSTM for time series data and the ability of the CNN model to extract local features, which reduces the loss of feature information and achieves a higher fault detection rate. A one-dimensional dense CNN is used as the main body of the CNN, and the LSTM network is sensitive to changes in sequence information to avoid model overfitting while building a deeper network. The maximum mutual information coefficient (MMIC) data preprocessing method is adopted to improve the local correlation of the data and improve the efficiency of the PLSTM-CNN model to detect faults from different initial conditions. The research results show that the parallel PLSTM-CNN has better prediction performance than the serial PLSTM-CNN, and its FDR and FPR are 90.5% and 0.051, respectively. It shows that the use of convolutional deep learning models for the prediction of writing grammar correction faults has strong application prospects.
KeywordsPLSTM-CNN; Fault detection; English writing; grammar; Deep learning.
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