Research Publications Authored by SLIIT Staff

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This collection includes all SLIIT staff publications presented at external conferences and published in external journals. The materials are organized by faculty to facilitate easy retrieval.

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Author: search.filters.author.Nugaliyadde, ASubject: search.filters.subject.Natural Language Processing

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    PublicationOpen Access
    Evolutionary algorithm for sinhala to english translation
    (arXiv preprint arXiv:1907.03202, 2019-07-06) Joseph, JK; Chathurika, W. M. T; Nugaliyadde, A; Mallawarachchi, Y
    Machine Translation (MT) is an area in natural language processing, which focus on translating from one language to another. Many approaches ranging from statistical methods to deep learning approaches are used in order to achieve MT. However, these methods either require a large number of data or a clear understanding about the language. Sinhala language has less digital text which could be used to train a deep neural network. Furthermore, Sinhala has complex rules therefore, it is harder to create statistical rules in order to apply statistical methods in MT. This research focuses on Sinhala to English translation using an Evolutionary Algorithm (EA). EA is used to identifying the correct meaning of Sinhala text and to translate it to English. The Sinhala text is passed to identify the meaning in order to get the correct meaning of the sentence. With the use of the EA the translation is carried out. The translated text is passed on to grammatically correct the sentence. This has shown to achieve accurate results
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    PublicationOpen Access
    Simplifying Law Statements Using Natural Language Processing
    (SLIIT, 2016-11-16) Dharmasiri, N; Gunathilake, B; Pathirana, u; Senevirathne, S; Nugaliyadde, A; Thelijjagoda, S
    Understanding the law statements for general public is evidently complex. The research derives a computational solution on reducing the complexity of the law statements. Given a law statement, the research will use both wordnet and “LawNet” to create a simpler meaning. The research will focus on information extraction, information retrieval, question analysis and answer generation techniques to derive better meaning of law statements. The law statement will be treated as a question and the “LawNet” and wordnet will be used in as information extraction points. The law statement will be analyzed as a question; more information will be retrieved through the wordnet and “LawNet”. This process mostly acts similar to a search engine’s process. The results provide on average 80% accuracy for a 1500 dataset.