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VGU RESEARCH REPOSITORY


Please use this identifier to cite or link to this item: https://epub.vgu.edu.vn/handle/dlibvgu/988
DC FieldValueLanguage
dc.contributor.authorDuong Thanh Hungen_US
dc.date.accessioned2021-03-08T08:10:24Z-
dc.date.accessioned2021-03-08T08:10:27Z-
dc.date.available2021-03-08T08:10:24Z-
dc.date.available2021-03-08T08:10:27Z-
dc.date.issued2020-
dc.identifier.urihttp://epub.vgu.edu.vn/handle/dlibvgu/988-
dc.description.abstractIn this thesis, we set out to investigate the task of relation extraction. In particular, we only concern about the relation classification step for binary relations. We conducted experiments with various settings, using BERT as the word representation. We also tested the idea of assigning "no relation" detection to a separate classifier. Overall, the single-classifier architecture yields the best performance. On the SemEval 2010 Task 8 dataset, it achieved the F1 score of 90.12%, and on the GDS dataset, it reached the average precision score of 85.05%. The duo classifier architecture does not fall far behind (89.98% F1 on the SemEval dataset)en_US
dc.language.isoenen_US
dc.rightsAttribution-NonCommercial 4.0 International*
dc.rights.urihttps://creativecommons.org/licenses/by-nc/4.0/*
dc.subjectNatural language processingen_US
dc.subjectRelation extractionen_US
dc.subjectSupervised machine learningen_US
dc.subjectSemEval 2010 Task 8 dataseten_US
dc.titleRelation extraction from text using supervised machine learningen_US
dc.typeThesisen_US
item.grantfulltextopen-
item.fulltextWith Fulltext-
item.languageiso639-1other-
Appears in Collections:Computer Science (CS)
Computer Science (CS)
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