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dc.contributor.authorKilimci, Zeynep Hilal
dc.contributor.authorAkyokuş, Selim
dc.date.accessioned2019-12-26T23:08:27Z
dc.date.available2019-12-26T23:08:27Z
dc.date.issued2019en_US
dc.identifier.citationKilimci, Z. H., & Akyokuş, S. (2019). The evaluation of word embedding models and deep learning algorithms for Turkish text classification. In 4th International Conference on Computer Science and Engineering (pp. 548-553). Piscataway, NJ: IEEE. http://dx.doi.org/10.1109/UBMK.2019.8907027en_US
dc.identifier.isbn9781728139647
dc.identifier.isbn9781728139630
dc.identifier.isbn9781728139654
dc.identifier.other19157683 (INSPEC)
dc.identifier.urihttp://dx.doi.org/10.1109/UBMK.2019.8907027
dc.identifier.urihttps://hdl.handle.net/11376/3511
dc.descriptionKilimci, Zeynep Hilal (Dogus Author) -- Conference full title: 4th International Conference on Computer Science and Engineering, UBMK 2019; Samsun; Turkey; 11 September 2019 through 15 September 2019.en_US
dc.description.abstractThe use of word embedding models and deep learning algorithms are currently the most common and popular trends to enhance the overall performance of a text classification/categorization system. Word embedding models are vectors that provide a mapping of words with similar meaning to own a similar representation which is learned from a corpus. Deep learning algorithms successful produce more successful results in many areas of their applications when they are compared to the conventional machine learning algorithms. In this study, three different word embedding models Word2Vec, Glove, and FastText are employed for word representation. Instead of using conventional classification algorithms, three different deep learning architectures Recurrent Neural Networks (RNN), Long Short Term Memory Networks (LSTM) and Convolutional Neural Networks (CNN) are used for classification task by performing experiments on collections of different Turkish documents. Experimental results show that the usage of deep learning algorithms together with word embedding models advances the performance of text classification systems.en_US
dc.language.isoengen_US
dc.publisherIEEEen_US
dc.identifier.doi10.1109/UBMK.2019.8907027en_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectWord2Vecen_US
dc.subjectGloveen_US
dc.subjectFastTexten_US
dc.subjectRecurrent Neural Networksen_US
dc.subjectLong Short Term Memoryen_US
dc.subjectConvolutional Neural Networksen_US
dc.subjectText Categorizationen_US
dc.titleThe evaluation of word embedding models and deep learning algorithms for Turkish text classificationen_US
dc.typeconferenceObjecten_US
dc.relation.journal4th International Conference on Computer Science and Engineeringen_US
dc.departmentDoğuş Üniversitesi, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.authoridhttps://orcid.org/0000-0003-1497-305Xen_US
dc.identifier.startpage548en_US
dc.identifier.endpage553en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.institutionauthorKilimci, Zeynep Hilal


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