Research and Implementation of English Grammar Check and Error Correction Based on Deep Learning

Abstract

English as a universal language in the world will get more and more attention, but English is not our mother tongue, and there exist differences in culture and thinking. English grammar is the most difficult problem to solve. There are many English learners, and the number of English teachers is limited, and it is inevitable to use Internet technology to solve the problem of lack of resources. The article uses deep learning technology to propose an ASS grammar detection model, which can quickly and efficiently detect grammatical errors. The research results show the following. (1) This study selects data from the GEC evaluation task and analyzes the four modules of article, noun, verb, and preposition through algorithms under different models. The results indicate the accuracy of the four modules. The recall rate has been improved to a certain extent, the accuracy rate of nouns is the highest, which can reach 63.99%, the accuracy rate of prepositions is improved to a lesser extent, and the inspection accuracy rate after improvement is 12.79%. (2) In the experiment to verify the effectiveness of the ASS grammar detection model, compared with the detection effect of the ordinary model, the accuracy of the ASS comprehensive inspection has been greatly improved. The comprehensive accuracy of the ordinary detection model is 28.01%, and the ASS model’s comprehensive accuracy rate of the inspection was 82.82%, and the accuracy rate was increased by 54.81%. The result shows that the performance of the ASS inspection model has been improved by leaps and bounds compared with the traditional model. (3) After transforming and upgrading the ASS model, the three models and other models obtained were run on the test set and the mixed test set, respectively. The results show that the accuracy, precision, recall, and F1 score of ASS model are the highest in the test set, which are 98.71%, 98.83%, 98.64%, and 98.73%, respectively, the Bayesian network check model has the lowest accuracy rate of 51.74%, and the ROC curve value and AUC value of the ASS model are both the largest. The accuracy of the ASS model on the mixed test set is also the highest, reaching 98.01%. The JaSt model on the mixed test set has a significant downward trend, with the accuracy rate dropping from 92.16% to 56.68%. It can be concluded that the ASS model can accurately and efficiently monitor grammatical errors.