Optimizing semantic error detection through weighted federated machine learning: A comprehensive approachopen access
- Authors
- Naz, N.S.; Abbas, S.; Khan, M.A.; Hassan, Z.; Bukhari, M.; Ghazal, T.M.
- Issue Date
- Jan-2024
- Publisher
- Institute of Advanced Science Extension (IASE)
- Keywords
- Artificial neural network; Federated learning; Natural language processing; SED-WFML; Semantic error detection
- Citation
- International Journal of Advanced and Applied Sciences, v.11, no.1, pp 150 - 160
- Pages
- 11
- Journal Title
- International Journal of Advanced and Applied Sciences
- Volume
- 11
- Number
- 1
- Start Page
- 150
- End Page
- 160
- URI
- https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/90806
- DOI
- 10.21833/ijaas.2024.01.018
- ISSN
- 2313-626X
2313-3724
- Abstract
- Recently, the improvement of network technology and the spread of digital documents have made the technology for automatically correcting English texts very important. In English language processing, finding and fixing mistakes in the meaning of words is a very interesting and important job. It is also important to fix wrong data in cleaning data. Usually, systems that find errors need the user to set up rules or statistical information. To build a good system for finding mistakes in meaning, it must be able to spot errors and odd details. Many things can make the meaning of a sentence unclear. Therefore, this study suggests using a system that finds semantic errors with the help of weighted federated machine learning (SED-WFML). This system also connects to the web ontology's classes and features that are important for the area of knowledge in natural language processing (NLP) text documents. This helps identify correct and incorrect sentences in the document, which can be used for many purposes like checking documents automatically, translating, and more. During its training and checking stages, the new model identified correct and incorrect sentences with an accuracy of 95.6% and 94.8%, respectively, which is better than earlier methods. © 2024 The Authors. Published by IASE. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
- Files in This Item
- There are no files associated with this item.
- Appears in
Collections - ETC > 1. Journal Articles
![qrcode](https://api.qrserver.com/v1/create-qr-code/?size=55x55&data=https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/90806)
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.