Building COVID Knowledge Base from Medical Journals

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dc.contributor.advisorMohammad Ashrafuzzaman Khan
dc.contributor.authorMd. Jubaer Khan
dc.contributor.authorWalidul Alam Ehab
dc.contributor.id1721616042
dc.contributor.id1631214042
dc.coverage.departmentElectrical and Computer Engineering
dc.date.accessioned2025-08-18
dc.date.accessioned2025-08-18T04:33:17Z
dc.date.available2025-08-18T04:33:17Z
dc.date.issued2021
dc.description.abstractNatural Language Processing (NLP), a subfield of linguistics, computer science and artificial intelligence, is concerned with the interactions between computers and human language, particularly with how computers are programmed to process and interpret large quantities of natural language data. In the covid pandemic, many research articles are being published every day, and scientists and doctors are trying to figure out the cure for saving humanity. Unfortunately, it is pretty hard to keep updated with all of the research that is being published. Our approach is to extract essential information from the research articles and find a relation between the disease and its symptoms using the Word2vec model. We have preprocessed a large number of journal article’s abstracts and train them into Word2vec model in our work. After getting the words vectorial representation, we have visualized the data for finding significant relationships among the disease and their symptoms-related words.
dc.description.degreeUndergraduate
dc.identifier.cd600000628
dc.identifier.print-thesisTo be assigned
dc.identifier.urihttps://repository.northsouth.edu/handle/123456789/1376
dc.language.isoen
dc.publisherNorth South University
dc.rights© NSU Library
dc.titleBuilding COVID Knowledge Base from Medical Journals
dc.typeThesis
oaire.citation.endPage32
oaire.citation.startPage1
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