Please use this identifier to cite or link to this item: https://rda.sliit.lk/handle/123456789/3361
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dc.contributor.authorSathsarani, M.W.A.R.-
dc.contributor.authorThalawaththa, T.P.A.B.-
dc.contributor.authorGalappaththi, N.K.-
dc.contributor.authorDanthanarayana, J.N.-
dc.contributor.authorGamage, A-
dc.date.accessioned2023-03-10T04:28:18Z-
dc.date.available2023-03-10T04:28:18Z-
dc.date.issued2022-12-21-
dc.identifier.isbn978-1-6654-5699-9-
dc.identifier.urihttps://rda.sliit.lk/handle/123456789/3361-
dc.description.abstractNatural Language Processing (NLP) is a sub-field of Artificial Intelligence (AI) that consists of a collection of computational methods motivated by theory for the automated classification and reflection of human languages. The foundation for many sophisticated applications of NLP, including named entity recognition, sentiment analysis, machine translation, in-formation retrieval, and information processing, is laid by Part of Speech (POS) tagging, which is part of the lexical layer of NLP systems. In contrast to English, French, German, and other languages from the same geographical region, the development of high-accuracy, stable POS taggers for the Sinhala language is still in its early stages. Hence, Sinhala is identified as a low-resource language. The main objective of this research is to create a POS tagger for the Sinhala language to solve this issue. An innovative and novel strategy that has never been used with the Sinhala language has been designed. This approach has been suggested specifically to evaluate the possibility of enhancing the accuracy compared to other methodologies. So, deep learning algorithms have been applied in this study, which has a significant impact on improving tagger performance. First, highly accurate individual classifiers for primary POS tags were implemented, and then they were combined into one composite model. As expected, all individual classifiers and the final composite model have achieved a higher accuracy level. Thus, it demonstrates that the proposed solution using deep learning algorithms outperformed other methods, such as rule-based and stochastic, in terms of accuracy.en_US
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.relation.ispartofseries: 2022 6th International Conference on Computation System and Information Technology for Sustainable Solutions (CSITSS);-
dc.subjectSinhalaen_US
dc.subjectPart of Speechen_US
dc.subjectTagger usingen_US
dc.subjectDeep Learningen_US
dc.subjectTechniquesen_US
dc.titleSinhala Part of Speech Tagger using Deep Learning Techniquesen_US
dc.typeArticleen_US
dc.identifier.doi10.1109/CSITSS57437.2022.10026395en_US
Appears in Collections:Department of Information Technology
Research Papers - IEEE
Research Papers - SLIIT Staff Publications
Research Publications -Dept of Information Technology

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