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dc.contributor.authorLin, Yanwenen_US
dc.identifier.citationLin, Y. (2022). Concept graph based semantic matching of articles. Final Year Project (FYP), Nanyang Technological University, Singapore.
dc.description.abstractNatural Language Processing (NLP) is a luring area to explore. It allows machines to directly “understand” natural language, therefore operation based on text can be processed without further disposal, and human orders can be taken and implemented by machines without further programming, which enhance the user-friendliness for many industries. Past years have seen a rapid improvement of NLP. In current NLP technology, keyword detection is widely used for matching articles. However, this method overlooked the semantics of articles. On the other hand, the existing models targeting at semantic analysis take up large computational capacity. In this project, Concept Interaction Graph (CIG), a model generating semantic graphs from articles, was studied.en_US
dc.publisherNanyang Technological Universityen_US
dc.subjectEngineering::Electrical and electronic engineering::Computer hardware, software and systemsen_US
dc.titleConcept graph based semantic matching of articlesen_US
dc.typeFinal Year Project (FYP)en_US
dc.contributor.supervisorLihui Chenen_US
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.description.degreeBachelor of Engineering (Electrical and Electronic Engineering)en_US
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Appears in Collections:EEE Student Reports (FYP/IA/PA/PI)
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