Design and Application of English Grammar Error Correction System Based on Deep Learning

Hongli, Chen

doi:10.1155/2021/4920461

Cited by 7 publications

(7 citation statements)

References 5 publications

(5 reference statements)

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“…The feedback filtering process shown in Figure 2 [2]. The application will correct the sentence first based on existing rules and send back to the users for feedback.…”

Section: Feedback Filtering Module-a Deep Learning-based Methodsmentioning

confidence: 99%

“…Traditional English grammar error correction system is based on machine learning and data mining [1]. Chen [2] gave a new attempt to use deep learning technology in order to solve the existing problems of the traditional system. The classification model is the choice of traditional English grammar error correction system [1] and in latest studies of deep models [3], the Transformer model performs better in detection and correction than most grammar models.…”

Section: Introductionmentioning

confidence: 99%

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The Investigation of Machine Learning in Grammar Correction

Dai

2024

LNEP

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As English continues to be the dominant language acquired by the majority of the global population, the demand for learning it is on the rise. Within English education, grammar plays a vital role and can now benefit from machine learning-based correctional applications. This paper explores various methods employed in prior research for grammar correction. Notably, the feature extraction method proves to be effective in capturing essential information from the text, resulting in more precise revisions of grammatical errors. The feedback filtering module will select valid improvement advice from users to develop more efficient application. Recurrent Neural Network (RNN) which is a widely-used model can also be adopted to grammar correction due to its memorizing ability. In previous studies, these methods are tested to see their validity in English grammar correction. Results of feedback filtering module show that it can sort out users advice into useful and useless so that the modification of the application can be more accurate. In another experiment, the F-0.5 score of RNN is measured with several other models and RNN has apparent advantage over the majority in grammar error detection and correction. Admittedly, however, these methods still have space for further enhancement to provide high precision in correction. Means to eliminate possible errors and inaccuracy are urged to be found, but probably the only way out is the innumerable data fed to computers. This paper offers a comprehensive view of current study progress in the field and encourage new evolution.

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“…The feedback filtering process shown in Figure 2 [2]. The application will correct the sentence first based on existing rules and send back to the users for feedback.…”

Section: Feedback Filtering Module-a Deep Learning-based Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The Investigation of Machine Learning in Grammar Correction

Dai

2024

LNEP

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“…e N-gram model is usually divided into two stages: the training stage and the inspection stage [9,10]. According to the needs of the model, the corpus information is counted and saved.…”

Section: English Grammatical Error Preprocessing Based On the N-gram ...mentioning

confidence: 99%

“…In simple terms, the task of grammatical error detection is to utilize a computer for the identi cation of grammatical errors along with their location and to classify or correct these errors [1]. It has a wide range of applications, including automated correction of language learner mistakes, content proofreading, and grammatical correction [2]. At present, the detection of English grammar errors usually relies on manual review by teachers or graders.…”

Section: Introductionmentioning

confidence: 99%

Identification of Grammatical Errors of English Language Based on Intelligent Translational Model

Hongli

2022

Mobile Information Systems

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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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“…English writing, as an inaccessible part of English learning, is related to the mastery of English grammar learning effect, and the effect of the display of English learning results [2]. In the current Chinese English education, due to the irrational structure of the teacher-student ratio and the students' social competitiveness needs, it is difficult for teachers to do meticulous corrections and guidance for the problems of writing counselling and language error correction, while the students do not have a good understanding of the English grammatical semantics and sentence structure, and they use the Chinese semantic structure and so on to fill them in, which makes the English expression wrong [3]. Therefore, the research and development of an English writing tutoring and grammar error correction performance evaluation model for Chinese students is of great necessity, which is not only conducive to the rational allocation of teachers' writing tutoring resources, but also more conducive to the timely and effective correction of students' grammatical errors [4].…”

Section: Introductionmentioning

confidence: 99%

Performance Evaluation and Improvement of Deep Echo State Network Models in English Writing Assistance and Grammar Error Correctionn

Chen

2024

ICST Transactions on Scalable Information Systems

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INTRODUCTION: The research on the performance evaluation model of English writing tutoring and grammar error correction is very necessary, which is not only conducive to the rational allocation of teachers' writing tutoring resources, but also more conducive to the timely and effective correction of students' grammatical errors.OBJCTIVES: Aiming at the problems of non-specific quantification, low precision, and low real-time performance evaluation methods for English writing grammar error correction in current methods.METHODS: This paper proposes a grammar error correction performance evaluation method based on deep echo state network with gold rush optimisation algorithm. Firstly, by analysing the process of English writing assistance and grammatical error correction, we extract the evaluation features of grammatical error correction type and construct the performance evaluation system; then, we improve the deep confidence network through the gold rush optimization algorithm and construct the grammatical error correction performance evaluation model; finally, we analyse it through simulation experiments.RESULTS: The results show that the proposed method improves the evaluation accuracy, robustness. The absolute value of the relative error of the evaluation value of the syntactic error correction performance of the method is controlled within the range of 0.02.CONCLUSION: The problems of non-specific quantification, low precision and low real-time performance of the application of English writing grammar error correction performance assessment methods are solved.

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Design and Application of English Grammar Error Correction System Based on Deep Learning

Cited by 7 publications

References 5 publications

The Investigation of Machine Learning in Grammar Correction

The Investigation of Machine Learning in Grammar Correction

Identification of Grammatical Errors of English Language Based on Intelligent Translational Model

Performance Evaluation and Improvement of Deep Echo State Network Models in English Writing Assistance and Grammar Error Correctionn

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