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Subject: search.filters.subject.Natural Language Processing (NLP)

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Now showing 1 - 3 of 3
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    PublicationOpen Access
    Solving Sinhala Language Arithmetic Problems using Neural Networks
    (arxiv logo > cs > arXiv:1809.04557, 2018-09-11) Chathurika, W. M. T; De Silva, K. C; Raddella, A. M; Ekanayake, E. M. R. S; Nugaliyadde, A; Mallawarachchi, Y
    A methodology is presented to solve Arithmetic problems in Sinhala Language using a Neural Network. The system comprises of (a) keyword identification, (b) question identification, (c) mathematical operation identification and is combined using a neural network. Naïve Bayes Classification is used in order to identify keywords and Conditional Random Field to identify the question and the operation which should be performed on the identified keywords to achieve the expected result. “One vs. all Classification” is done using a neural network for sentences. All functions are combined through the neural network which builds an equation to solve the problem. The paper compares each methodology in ARIS and Mahoshadha to the method presented in the paper. Mahoshadha2 learns to solve arithmetic problems with the accuracy of 76%.
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    PublicationOpen Access
    Guru Gedara: Smart Mathematical e-learning Platform for Grade Five Students
    (Department of Computing and Information Systems, Faculty of Applied Sciences, Sabaragamuwa University of Sri Lanka, PO Box 02, Belihuloya, 70140, Sri Lanka., 2021-02-24) Rajapaksha, S. K; Thilakarthna, T; Wijesundara, W. G. M. V. S; Wickramasooriya, M, K; Ekanayake, E. M. O. M; Katupitiya, S. R. M. J. S
    This paper introduces a web-based educational support system developed aiming optimize mathematical knowledge of Grade 5 students. This application can be also used as a distance learning method for the lockdown situations occurred due to COVID-19 pandemic. Both primary class students and teachers face various difficulties, due to competitiveness of grade 5 scholarship examination. Consequently, emotion analysis which is a sub section in this system, uses sentiment analyzing to boosts the students’ emotional state, while Automated MCQ generated sub section can be used to gain mathematical knowledge, by facing MCQ quizzes. Additionally, Smart Video-based Learning Environment sub section can be used to gain knowledge related to mathematics by searching relevant videos, besides the Voicebased Question Answering section can be used to get answers orally for the questions asked in voice format. However, some sections use IQ level of each student to provide better solutions. Above all, it can be used at home as an online service while identifying and solving student’s weakness.
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    PublicationEmbargo
    Ontological Knowledge Inferring Approach based on Term-Clustering and Intra-Cluster Permutations
    (2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10)
    Ontological representation of knowledge has the advantage of being easy to reason with, but ontology construction with knowledge facts, automatically acquiring them from open domain text is often challenging. This research introduces a novel approach to infer new ontological knowledge in a fully automated manner. Such ontological knowledge can be utilized in both constructing new ontologies and extending existing ontologies. Basic level triples that can be extracted from open domain text are used as the data source for this study. A simple mechanism has been introduced to convert the triple into an ontological knowledge fact and such ontological knowledge facts are further processed to infer new ontological knowledge. The main focus of this research is to infer new ontological knowledge using an advanced term-clustering mechanism followed by an intra-cluster permutation generation task. Generated permutations are potential to be selected as good ontological knowledge facts. Inferred ontological knowledge was tested with inter-rater agreement method with high reliability and variability. Results demonstrated that, out of 43,103 triples, this method inferred 127,874 ontological knowledge (approximately 3 times) of which 66% were estimated to be effective. Finally, this research contributes a reliable approach which requires a single pass over the corpus of triples to infer a large number of ontological knowledge facts that can be used to construct/extend ontologies.