Bahcevan, Cenk AnilKutlu, EmirhanYildiz, Tugba2024-07-182024-07-182018978-1-5386-7893-0https://hdl.handle.net/11411/82923rd International Conference on Computer Science and Engineering (UBMK) -- SEP 20-23, 2018 -- Sarajevo, BOSNIA & HERCEGParts 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.eninfo:eu-repo/semantics/closedAccessPart Of Speech TaggingRecurrent Neural NetworkLong-Short Term MemoryDeep LearningFasttextConference Object2-s2.0-85060639088238N/A235N/AWOS:000459847400044