Automatic Spelling Correction based on n-Gram Model

In this paper, a misspelling detection and correction system was developed for Ethiopian languages (Amharic, Afan Oromo, Tigrinya, Hadiyyisa, Kambatissa, and Awngi). For some of these languages, there have been few works on typo detection and correction systems. However, an effective and all-in-one typo detector and corrector system for Ethiopian languages have yet to be developed. A dictionary-based methodology is used to detect and rectify various forms of misspelling-related issues. The major characteristics of the proposed model can be outlined by presenting suggestions for detected flaws and automatically correcting them utilizing the first suggestion. In addition, the proposed model is evaluated using dictionary-based data sets for all languages. The corpora used were gathered from a variety of sources, including economic, political, social, and related publications, newspapers, and magazines. In this model, the users can perform all spelling-related issues within a single system (all-in-one). That means if the user(s) is (are) working on the Amharic language and then he/she/they can change the language she/he/they prefer(s) without shifting to another graphical user interface (GUI). Here, the users can save time and perform their tasks easily. Similarly, the user(s) can improve their skills in the selected languages accordingly. Finally, precision, recall, and f-measures for each language have been computed following a successful evaluation of the model. The system outperforms an f-measure of 89.57%, 87.57%, 88.31%, 86.83%, 81.83%, and 87.59% for Amharic, Afan Oromo, Tigrinya, Hadiyyisa, Kambatissa, and Awngi languages respectively. Furthermore, recommendations have been provided for future researchers.

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An Automated Word Spelling Error Detection System

Ogechi Okeh

2024

International Journal of Innovative Science and Research Techno

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Accurate spelling has extensive consequences for written communication's clarity, credibility, and general effectiveness in several contexts, including academic and professional writing and personal communications. The frequency of spelling errors has increased significantly recently, mainly owing to the widespread use of digital media and society's dependence on written communication. Misspelled words can diminish the reliability of written correspondence and result in misinterpretations. Hence, there is an urgent requirement for an innovative and advanced solution to improve the identification of spelling errors. This research seeks to address these challenges and develop a system that significantly enhances the quality of written communication by improving the accuracy and relevance of spelling error detection and correction suggestions. According to this study, the implemented model is a promising technique to correct spelling problems in legal terminology, has been praised for its effectiveness and usability. Its recommendations include continuous research and development, improving its ability to use contextual cues and domain-specific information for error correction, emphasizing user-centric customization, implementing collaborative feedback systems, offering extensive training and support, prioritizing ethical and regulatory considerations, and fostering interdisciplinary collaboration. These suggestions aim to optimize the Spello model's performance, resilience, and scalability, and make it a fundamental technology in promoting accuracy, integrity, and accessibility in the legal profession and society. The model can become a fundamental technology in promoting accuracy, integrity, and accessibility in the legal profession and society through combined efforts and collaboration.

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Automatic Spelling Correction based on n-Gram Model

Cited by 3 publications

References 20 publications

Transformer-Based Neural Machine Translation for Post-OCR Error Correction in Cursive Text

Transformer-Based Neural Machine Translation for Post-OCR Error Correction in Cursive Text

Automated all in one misspelling detection and correction system for Ethiopian languages

An Automated Word Spelling Error Detection System

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