Deep Learning-Based Context-Sensitive Spelling Typing Error Correction

Lee, Jung‐Hun; Kim, Minho; Kwon, Hyuk‐Chul

doi:10.1109/access.2020.3014779

Cited by 28 publications

(18 citation statements)

References 12 publications

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“…According to the user use case diagram and the design objectives of English grammar error correction system, the main functions of English grammar error correction system are combined and analyzed below for subsequent system modeling and module division [10].…”

Section: System Business Requirement Function Analysismentioning

confidence: 99%

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

Hongli

2021

Security and Communication Networks

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In order to solve the problems of low correction accuracy and long correction time in the traditional English grammar error correction system, an English grammar error correction system based on deep learning is designed in this paper. This method analyzes the business requirements and functions of the English grammar error correction system and then designs the overall architecture of the system according to the analysis results, including English grammar error correction module, service access module, and feedback filtering module. The multilayer feedforward neural network is used to construct the language model to judge whether the language sequence is a normal sentence, so as to complete the correction of English grammatical errors. The experimental results show that the designed system has high accuracy and fast speed in correcting English grammatical errors.

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Section: System Business Requirement Function Analysismentioning

confidence: 99%

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

Hongli

2021

Security and Communication Networks

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“…The spelling correction technique was used by [11,12] in their work for converting (normalizing) noisy words into meaningful replacements. The techniques that are used for spelling correction are edit distance [48,49], similarity key [48,49], rule-based [48,49], N-gram [49], probabilistic [49], neural network [50], and auto-encoders [51].…”

Section: Background and Related Workmentioning

confidence: 99%

Enhancement of Text Analysis Using Context-Aware Normalization of Social Media Informal Text

Khan

Lee

2021

Applied Sciences

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We proposed an application and data variations-independent, generic social media Textual Variations Handler (TVH) to deal with a wide range of noise in textual data generated in various social media (SM) applications for enhanced text analysis. The aim is to build an effective hybrid normalization technique that ensures the use of useful information of the noisy text in its intended form instead of filtering them out to analyze SM text better. The proposed TVH performs context-aware text normalization based on intended meaning to avoid the wrong word substitution. We integrate the TVH with state-of-the-art (SOTA) deep-learning-based text analysis methods to enhance their performance for noisy SM text data. The proposed scheme shows promising improvement in the text analysis of informal SM text in terms of precision, recall, accuracy, and F1-score in simulation.

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“…First of all, there are two main studies by Wood Kevin (2020) and Lee et al (2020) [21,22]. Frank has developed RGL (Restricted Grammar Language) for cloud-based agile document work.…”

Section: Review Of Advanced Research For Bdamentioning

confidence: 99%

“…Chae et al, (2019) analyzed the standardization compliance of Web (World Wide Web) documents assumed as a business document and suggested a way to effectively correct the errors with RPA [26]. First of all, there are two main studies by Wood Kevin (2020) and Lee et al, (2020) [21,22]. Frank has developed RGL (Restricted Grammar Language) for cloud-based agile document work.…”

Section: Review Of Advanced Research For Bdamentioning

confidence: 99%

Improvement of Business Productivity by Applying Robotic Process Automation

et al. 2021

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Digitalization has been bringing about various changes and innovations not only in our daily life but also in our business environment. In the manufacturing industry, robots have been used for automation for a long time, resulting in innovation in terms of the faster operation process and higher product quality. Robotics Process Automation (RPA) can be said to have brought this innovation in the productivity improvement of many industries into the business office. The purpose of this study is to improve business productivity by applying RPA named CoPA. It is based on Domain-Specific Languages (DSLs) and Model-Driven Engineering (MDE) coupled with MS Office. CoPA has been replaced to perform the repetitive patterned tasks (especially document work) done by many people in an office. For the applications of business productivity, CoPA has been implemented to revise five government project proposals requiring quite strict writing standards. The improvement of business productivity obtained by CoPA has been compared to the performance of 10 employees who are familiar with MS Office. The paper explains the method of CoPA coupled with MS Office as well as the agile method of human collaboration. It is clearly shown that CoPA as a business RPA can improve business productivity in terms of time consumption and document quality.

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Deep Learning-Based Context-Sensitive Spelling Typing Error Correction

Abstract: His research interests include natural language processing, information retrieval, machine learning and its application.

Cited by 28 publications

References 12 publications

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

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

Enhancement of Text Analysis Using Context-Aware Normalization of Social Media Informal Text

Improvement of Business Productivity by Applying Robotic Process Automation

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