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

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 outperforms RNN with 88.7% F1-score. This study is the first study that contributes to the literature utilizing word embedding and NNLM for the Turkish language.