Hema M1* , Kandasamy Sellamuthu2 , Vijayarajeswari R3
1Department of English, KPR Institute of Engineering and Technology, Coimbatore, India. *Corresponding author.
2Department of CSE, KPR Institute of Engineering and Technology, Arasur, Coimbatore-641407, India.
3Department of Computer Science and Engineering, Velalar College of Engineering and Technology, India.
Rupkatha Journal, Vol. 16, Issue 2, 2024. https://doi.org/10.21659/rupkatha.v16n2.04
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Abstract
In English, grammatical errors pose a significant challenge, prompting the exploration of diverse detection and correction methods. Existing approaches, however, often fall short of delivering satisfactory results and achieving high accuracy. An innovative solution, the Optimized Graph Dual Encoder Decoder with Pyramid Attention (OGDED-PA), is introduced to overcome these limitations. The model utilizes the C4_200M synthetic dataset for input data, followed by preprocessing and applying hybrid Squared Root of Term Frequency Variants with Mean Semi-absolute Deviation Factors for morphological feature extraction. Bidirectional long short-term memory with conditional random field segmentation is employed, and OGDED-PA, integrating a dual encoder-decoder architecture and pyramid attention mechanism, is then applied. This model aims to enhance accuracy in identifying and correcting grammar, syntax, punctuation, and spelling errors by capturing intricate linguistic patterns. The graph-based representation leverages Improved Border Collie Optimization (IBCO) to optimize the weight parameter, allowing the model to analyze syntactic and semantic relationships and address a broad spectrum of grammatical errors. The proposed method is implemented using the Python platform. Compared to existing methods, the proposed approach achieves 99.3% accuracy, 98.7% precision and 98.6% F0.5.
Keywords: English grammatical error detection and correction, Morphological features, Pyramid attention mechanism, Improved Border Collie Optimization, Dual encoder and decoder
| Conflicts of Interest: The authors declared no conflicts of interest. Ethical Consideration: Informed consent was obtained from all the participants of the study. Funding: No funding was received for this research. Article History: Received: 08 March 2024. Revised: 27 June 2024. Accepted: 28 June 2024. First published: 29 June 2024. Copyright: © 2024 by the author/s. License: License Aesthetix Media Services, India. Distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Published by: Aesthetix Media Services, India Citation: Hema, M. & Sellamuthu, K. & Vijayarajeswari, R. (2024). Detection of English Grammatical Errors and Correction using Graph Dual Encoder Decoder with Pyramid Attention Network. Rupkatha Journal 16:2. https://doi.org/10.21659/rupkatha.v16n2.04 |
