  Natural language is one of the most fundamental features that distinguish
people from other living things and enable people to communicate each other.
Language is a tool that enables people to express their feelings and thoughts
and to transfers cultures through generations. Texts and audio are examples of
natural language in daily life. In the natural language, many words disappear
in time, on the other hand new words are derived. Therefore, while the process
of natural language processing (NLP) is complex even for human, it is difficult
to process in computer system. The area of linguistics examines how people use
language. NLP, which requires the collaboration of linguists and computer
scientists, plays an important role in human computer interaction. Studies in
NLP have increased with the use of artificial intelligence technologies in the
field of linguistics. With the deep learning methods which are one of the
artificial intelligence study areas, platforms close to natural language are
being developed. Developed platforms for language comprehension, machine
translation and part of speech (POS) tagging benefit from deep learning
methods. Recurrent Neural Network (RNN), one of the deep learning
architectures, is preferred for processing sequential data such as text or
audio data. In this study, Turkish POS tagging model has been proposed by using
Bidirectional Long-Short Term Memory (BLSTM) which is an RNN type. The proposed
POS tagging model is provided to natural language researchers with a platform
that allows them to perform and use their own analysis. In the development
phase of the platform developed by using BLSTM, the error rate of the POS
tagger has been reduced by taking feedback with expert opinion.
