Automated Spelling Checker And Grammatical Error Detection And Correction Model for Sinhala Language

dc.contributor.authorGoonawardena, Mithma
dc.contributor.authorKulatunga, Ashini
dc.contributor.authorWickramasinghe, Raveena
dc.contributor.authorWeerasekara, Thisuraka
dc.contributor.authorDe Silva, Hansi
dc.contributor.authorThelijjagoda, Samantha
dc.date.accessioned2022-10-31T09:17:33Z
dc.date.available2022-10-31T09:17:33Z
dc.date.issued2022
dc.description.abstractSinhala is a native language spoken by the Sinhalese people, the largest ethnic group in Sri Lanka. It is a morphologically rich language, which is a derivation of Pali and Sanskrit. The Sinhala language creates a diglossia situation, as the language’s written form differs from its spoken form. With this difference, the written form requires more complex rules to be followed when in use. Manually proofreading the content of Sinhala material takes up much time and labor, and it can be a tedious task. Hence, a system is necessary which can be used by different industries such as journalism and even students. At present, there are a handful of systems and research that have automated Sinhala spelling analysis and grammar analysis. In addition, the existing systems are mainly focused on either spelling analysis or grammar analysis. However, the proposed system will cover both aspects and improve upon existing work by either optimizing or re-building the process to provide accurate outputs. The proposed system consists of a suffix list built for verbs and subjects, which helps the system stand out from the current proposed solutions. This research intends to implement a service for spell checking and grammar correctness of formal context in Sinhala. The research follows a rule-based approach with some components adopting a hybrid approach. As per the literature survey, many papers were analyzed, related to different aspects of the proposed system and complete systems. The proposed system would be able to overcome most barriers faced by previous papers whilst it takes a fresh take on providing a solution.en_US
dc.identifier.citationGoonawardena Mithma; Kulatunga Ashini; Wickramasinghe Raveena; Weerasekara Thisuraka; De Silva Hansi; Thelijjagoda Samantha (2022), Automated Spelling Checker And Grammatical Error Detection And Correction Model for Sinhala Language, International Research Conference on Smart Computing and Systems Engineering (SCSE 2022), Department of Industrial Management, Faculty of Science, University of Kelaniya Sri Lanka. 184-189.en_US
dc.identifier.urihttp://repository.kln.ac.lk/handle/123456789/25426
dc.publisherDepartment of Industrial Management, Faculty of Science, University of Kelaniya Sri Lankaen_US
dc.subjectautomated Sinhala spelling analysis, grammar analysis, part of speech tagging, subject characteristics analysisen_US
dc.titleAutomated Spelling Checker And Grammatical Error Detection And Correction Model for Sinhala Languageen_US

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