Deep Neural Network Architecture for Part-of-Speech Tagging for Turkish Language

被引：0

作者：

Bahcevan, Cenk Anil ^{[1
]}

Kutlu, Emirhan ^{[2
]}

Yildiz, Tugba ^{[2
]}

机构：

[1] Istanbul Bilgi Univ, Dept Business Informat, Istanbul, Turkey

[2] Istanbul Bilgi Univ, Dept Comp Engn, Istanbul, Turkey

来源：

2018 3RD INTERNATIONAL CONFERENCE ON COMPUTER SCIENCE AND ENGINEERING (UBMK) | 2018年

关键词：

Part of Speech Tagging; Recurrent Neural Network; Long-Short Term Memory; Deep Learning; fastText;

D O I：

暂无

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

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.

引用

页码：235 / 238

页数：4

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