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Improvement of General Inquirer Features with Quantity Analysis

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dc.contributor.author Karadeniz, Talha
dc.contributor.author Doğdu, Erdoğan
dc.date.accessioned 2020-04-13T13:36:59Z
dc.date.available 2020-04-13T13:36:59Z
dc.date.issued 2018
dc.identifier.citation Dogdu, Erdogan; Karadeniz, Talha, "Improvement of General Inquirer Features with Quantity Analysis", 2018 IEEE International Conference on Big Data (Big Data), pp. 2228-2231, (2018). tr_TR
dc.identifier.issn 2639-1589
dc.identifier.uri http://hdl.handle.net/20.500.12416/3095
dc.description.abstract General Inquirer is a word-affect association vocabulary having 11896 entries. Ranging from rectitude to expressiveness, it comes with a flavor of categories. Despite the extensive content, a mapping from "To be or not to be." to "How much?" can be beneficial for word representation. In this work, we apply a method of window based analysis to obtain real valued General Inquirer attributes. Sentence Completion task is chosen to calculate the effectiveness of the operation. After whitening post-process, total cosine similarity convention is followed to concentrate on embedding improvement. Results indicate that our quantity focused variant is considerable. tr_TR
dc.language.iso eng tr_TR
dc.publisher IEEE tr_TR
dc.rights info:eu-repo/semantics/closedAccess tr_TR
dc.subject Sentence Completion tr_TR
dc.subject Word Embedding tr_TR
dc.title Improvement of General Inquirer Features with Quantity Analysis tr_TR
dc.type conferenceObject tr_TR
dc.relation.journal 2018 IEEE International Conference On Big Data (Big Data) tr_TR
dc.identifier.startpage 2228 tr_TR
dc.identifier.endpage 2231 tr_TR
dc.contributor.department Çankaya Üniversitesi, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü tr_TR


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