Multi-Head Attention Based Bidirectional LSTM for Spelling Error Detection in the Indonesian Language

Yanfi, Yanfi; Soeparno, Haryono; Setiawan, Reina; Budiharto, Widodo

doi:10.1109/access.2024.3422318

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2024

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Cited by 2 publications

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Machine Vision-based English Translation Grammar Error Detection Using Temporal Convolutional Network-Recurrent Neural Network

Li,

Lv,

Chang

et al. 2024

2024 International Conference on Intelligent Algorithms for Computational Intelligence Systems (IACIS)

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Machine Vision-based English Translation Grammar Error Detection Using Temporal Convolutional Network-Recurrent Neural Network

Li,

Lv,

Chang

et al. 2024

2024 International Conference on Intelligent Algorithms for Computational Intelligence Systems (IACIS)

View full text Add to dashboard Cite

Development of an Artificial Intelligence Based Correction System for Spelling Errors in Product Reviews

Çiftçi,

Nayir,

Ayan

et al. 2024

Scientific Journal of Mehmet Akif Ersoy University

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E-commerce has experienced rapid growth in recent years and continues to expand dynamically. In this sector, maximizing customer satisfaction and enhancing the shopping experience are recognized as important strategic initiatives. To maximize customer satisfaction, it is essential to accurately determine customer needs and provide appropriate solutions to meet demand. In this context, feedback obtained from customers holds significant importance. However, customer comments often contain spelling errors, complicating the analysis of these comments. This study aims to automatically correct spelling errors in user comments regarding products sold on the e-commerce site Trendyol.com. For this purpose, a system based on transformer architecture has been created. Various spelling error detection and correction models were subsequently developed based on this architecture. Prediction models have been developed using two separate datasets consisting of Trendyol user comments and two additional datasets, including the Turkish Spelling Check Dataset taken from the Hunspell library, and the effects of these four datasets on prediction performance have been examined. The success of the models has been evaluated using the Accuracy metric. The performance of the developed models was also compared with that of the model in the Zemberek library. As a result of the study, it has been observed that the utilization of the Turkish Spelling Check Dataset positively influences prediction performance. The developed system enhanced customer experience by correcting spelling errors in comments.

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Multi-Head Attention Based Bidirectional LSTM for Spelling Error Detection in the Indonesian Language

Cited by 2 publications

References 69 publications

Machine Vision-based English Translation Grammar Error Detection Using Temporal Convolutional Network-Recurrent Neural Network

Machine Vision-based English Translation Grammar Error Detection Using Temporal Convolutional Network-Recurrent Neural Network

Development of an Artificial Intelligence Based Correction System for Spelling Errors in Product Reviews

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