Download pdf
The Investigation of Machine Learning in Grammar Correction
- 1 Fudan University
* Author to whom correspondence should be addressed.
Abstract
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.
Keywords
Grammar Correction, Machine Learning, Artificial Intelligence
[1]. Shanchun, Z., Wei, L. (2021) English Grammar Error Correction Algorithm Based on Classification Model. COMPLEXITY.
[2]. Chen, H. (2021) Design and Application of English Grammar Error Correction System Based on Deep Learning. Security and Communication Networks.
[3]. Zhu, J., et al. (2021) Machine learning-based grammar error detection method in English composition, Scientific Programming, vol. 2021, pp. 1–10.
[4]. Khalid, S., Khalil, T., & Nasreen, S. (2014, August). A survey of feature selection and feature extraction techniques in machine learning. In 2014 science and information conference (pp. 372-378). IEEE.
[5]. Çayir, A., Yenidoğan, I., & Dağ, H. (2018, September). Feature extraction based on deep learning for some traditional machine learning methods. In 2018 3rd International conference on computer science and engineering (UBMK) (pp. 494-497). IEEE.
[6]. Naveenkumar, M., & Kaliappan, V. K. (2019, November). Audio based Emotion Detection and Recognizing Tool Using Mel Frequency based Cepstral Coefficient. In Journal of Physics: Conference Series (Vol. 1362, No. 1, p. 012063). IOP Publishing.
[7]. Zhang, A. (2022). Analysis of the Application of Feedback Filtering and Seq2Seq Model in English Grammar. Wireless Communications and Mobile Computing, Volume 2022, Article ID 9530379, 8 pages. Retrieved from https://doi.org/10.1155/2022/9530379
[8]. Joachims, T. (1998). Making large-scale SVM learning practical (No. 1998, 28). Technical report.
[9]. Myles, A. J., Feudale, R. N., Liu, Y., Woody, N. A., & Brown, S. D. (2004). An introduction to decision tree modeling. Journal of Chemometrics: A Journal of the Chemometrics Society, 18(6), 275-285.
[10]. Medsker, L. R., & Jain, L. C. (2001). Recurrent neural networks. Design and Applications, 5(64-67), 2.
[11]. Yu, Y., Si, X., Hu, C., & Zhang, J. (2019). A review of recurrent neural networks: LSTM cells and network architectures. Neural computation, 31(7), 1235-1270.
Cite this article
Dai,Z. (2024). The Investigation of Machine Learning in Grammar Correction. Lecture Notes in Education Psychology and Public Media,35,311-316.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
Disclaimer/Publisher's Note
The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of EWA Publishing and/or the editor(s). EWA Publishing and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
About volume
Volume title: Proceedings of the 2nd International Conference on Interdisciplinary Humanities and Communication Studies
© 2024 by the author(s). Licensee EWA Publishing, Oxford, UK. This article is an open access article distributed under the terms and
conditions of the Creative Commons Attribution (CC BY) license. Authors who
publish this series agree to the following terms:
1. Authors retain copyright and grant the series right of first publication with the work simultaneously licensed under a Creative Commons
Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this
series.
2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the series's published
version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial
publication in this series.
3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and
during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See
Open access policy for details).