The evaluation of word embedding models and deep learning algorithms for Turkish text classification

Basit Öğe Kaydı
dc.authorid0000-0003-0793-1601
dc.contributor.authorKilimci, Zeynep Hilal
dc.contributor.authorAkyokuş, Selim
dc.date.accessioned2022-04-05T07:21:58Z
dc.date.available2022-04-05T07:21:58Z
dc.date.issued2019
dc.departmentİstanbul Medipol Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümü
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 fur 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.
dc.identifier.citationKilimci, Z. H. ve Akyokuş, S. (2019). The evaluation of word embedding models and deep learning algorithms for Turkish text classification. 4th International Conference on Computer Science and Engineering (UBMK) içinde (548-553. ss.). Samsun, Turkey, September 11-15, 2019. https://dx.doi.org/10.1109/UBMK.2019.8907027
dc.identifier.doi10.1109/UBMK.2019.8907027
dc.identifier.endpage553
dc.identifier.isbn9781728139647
dc.identifier.scopusqualityN/A
dc.identifier.startpage548
dc.identifier.urihttps://dx.doi.org/10.1109/UBMK.2019.8907027
dc.identifier.urihttps://hdl.handle.net/20.500.12511/9216
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorAkyokuş, Selim
dc.language.isoen
dc.publisherIEEE (Institute of Electrical and Electronics Engineers)
dc.relation.ispartof4th International Conference on Computer Science and Engineering (UBMK)en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/embargoedAccess
dc.subjectConvolutional Neural Networks
dc.subjectFasttext
dc.subjectGlove
dc.subjectLong Short Term Memory
dc.subjectRecurrent Neural Networks
dc.subjectText Categorization
dc.subjectWord2Vec
dc.titleThe evaluation of word embedding models and deep learning algorithms for Turkish text classification
dc.typeConference Object

Dosyalar

Orijinal paket
Listeleniyor 1 - 1 / 1
Küçük Resim Yok
İsim:
Akyokuş-Selim-2019.pdf
Boyut:
442.11 KB
Biçim:
Adobe Portable Document Format
Açıklama:
Tam Metin / Full Text
İndir
Lisans paketi
Listeleniyor 1 - 1 / 1
Küçük Resim Yok
İsim:
license.txt
Boyut:
1.44 KB
Biçim:
Item-specific license agreed upon to submission
Açıklama:
İndir

Koleksiyon

Bildiri Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu
WoS İndeksli Yayınlar Koleksiyonu