Detection of English Grammatical Errors and Correction using Graph Dual Encoder Decoder with Pyramid Attention Network.
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...
| Publicado en: | Rupkatha Journal on Interdisciplinary Studies in Humanities Vol. 16; no. 2; pp. 1 - 25 |
|---|---|
| Autores principales: | , , |
| Formato: | Artículo |
| Publicado: |
Rupkatha Journal on Interdisciplinary Studies in Humanities
2024
|
| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=178493018&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 178493018 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 09752935 BGFH jtl: Rupkatha Journal on Interdisciplinary Studies in Humanities issn: 09752935 maglogo: N pubinfo: dt: 2024 vid: 16 iid: 2 pid: 68366 pub: Rupkatha Journal on Interdisciplinary Studies in Humanities artinfo: ui: 178493018 10.21659/rupkatha.v16n2.04 ppf: 1 ppct: 24 formats: tig: atl: Detection of English Grammatical Errors and Correction using Graph Dual Encoder Decoder with Pyramid Attention Network. aug: au: M., Hema Sellamuthu, Kandasamy R., Vijayarajeswari affil: Department of English, KPR Institute of Engineering and Technology, Coimbatore, India Department of CSE, KPR Institute of Engineering and Technology, Arasur, Coimbatore-641407, India Department of Computer Science and Engineering, Velalar College of Engineering and Technology, India su: English grammar Decoders & decoding Feature extraction Data analysis Graph theory sug: subj: English grammar Decoders & decoding Feature extraction Data analysis Graph theory keyword: Dual encoder and decoder English grammatical error detection and correction Improved Border Collie Optimization Morphological features Pyramid attention mechanism ab: 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. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y dt: @attributes: year: 2024 holdings: @attributes: islocal: N |
|---|