  Agglutinative languages such as Turkish, Finnish and Hungarian require
morphological disambiguation before further processing due to the complex
morphology of words. A morphological disambiguator is used to select the
correct morphological analysis of a word. Morphological disambiguation is
important because it generally is one of the first steps of natural language
processing and its performance affects subsequent analyses. In this paper, we
propose a system that uses deep learning techniques for morphological
disambiguation. Many of the state-of-the-art results in computer vision, speech
recognition and natural language processing have been obtained through deep
learning models. However, applying deep learning techniques to morphologically
rich languages is not well studied. In this work, while we focus on Turkish
morphological disambiguation we also present results for French and German in
order to show that the proposed architecture achieves high accuracy with no
language-specific feature engineering or additional resource. In the
experiments, we achieve 84.12, 88.35 and 93.78 morphological disambiguation
accuracy among the ambiguous words for Turkish, German and French respectively.
