Deep Neural Network Architecture for Part-of-Speech Tagging for Turkish Language
Küçük Resim Yok
Tarih
2018
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
IEEE
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Parts of Speech (POS) tagging is one of the most well-studied problems in the field of Natural Language Processing (NLP). In this paper, a Neural Network Language Models (NNLM) such as Recurrent Neural Network (RNN) and Long Short Term Memory (LSTM) have been trained and assessed to address the POS tagging problem for the Turkish Language. The performance is compared to the state-of-art methods. The results show that LSTM outperl4ms RNN with 88.7% Fl-score. This study is the first study that contributes to the literature utilizing word embedding and NNLM for the Turkish language.
Açıklama
3rd International Conference on Computer Science and Engineering (UBMK) -- SEP 20-23, 2018 -- Sarajevo, BOSNIA & HERCEG
Anahtar Kelimeler
Part Of Speech Tagging, Recurrent Neural Network, Long-Short Term Memory, Deep Learning, Fasttext
Kaynak
2018 3rd International Conference on Computer Science and Engineering (Ubmk)
WoS Q Değeri
N/A
Scopus Q Değeri
N/A
