Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.11861/7428
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dc.contributor.authorLai, Sunnyen_US
dc.contributor.authorProf. LEUNG Kwong Saken_US
dc.contributor.authorLeung, Yeeen_US
dc.date.accessioned2023-02-22T11:09:32Z-
dc.date.available2023-02-22T11:09:32Z-
dc.date.issued2018-
dc.identifier.citationNAACL HLT 2018 - International Workshop on Semantic Evaluation, SemEval 2018 - Proceedings of the 12th Workshop pp. 741-746en_US
dc.identifier.isbn978-194808720-9-
dc.identifier.urihttp://hdl.handle.net/20.500.11861/7428-
dc.description.abstractWe present SUNNYNLP, our system for solving SemEval 2018 Task 10: “Capturing Discriminative Attributes”. Our Support-Vector-Machine(SVM)-based system combines features extracted from pre-trained embeddings and statistical information from Is-A taxonomy to detect semantic difference of concepts pairs. Our system is demonstrated to be effective in detecting semantic difference and is ranked 1st in the competition in terms of F1 measure. The open source of our code is coined SUNNYNLP. © 2018 Association for Computational Linguisticsen_US
dc.language.isoenen_US
dc.titleSUNNYNLP at SemEval-2018 Task 10: A Support-Vector-Machine-Based Method for Detecting Semantic Difference using Taxonomy and Word Embedding Featuresen_US
dc.typeConference Paperen_US
dc.relation.conferenceNAACL HLT 2018 - International Workshop on Semantic Evaluation, SemEval 2018 - Proceedings of the 12th Workshopen_US
item.fulltextNo Fulltext-
crisitem.author.deptDepartment of Applied Data Science-
Appears in Collections:Applied Data Science - Publication
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