Effectiveness Analysis of Automatic Detection and Correction Algorithms for English Grammatical Errors

Aiming at the common grammatical errors in English writing, this paper constructs a large-scale corpus containing various grammatical errors, and cleans and classifies the data through manual annotation. Then, a deep learning model combining CNN (convolutional neural network) and RNN (recurrent neural network) is used to train the error types in the corpus to automatically identify and mark grammatical errors. Finally, the detection results output by the model are compared with the manual proofreading results to calculate the correction effect. The model has a high accuracy and recall rate in the detection of word form errors in long sentences, with an F1 value of 0.93. This study shows that the automatic detection and correction algorithm of grammatical errors based on deep learning has significant advantages in improving the writing accuracy of English learners.