Abstract
We develop a language-independent, deep learning-based approach to the task of morphological disambiguation.Guided by the intuition that the correct analysis should be "most similar" to the context, we propose dense representations for morphological analyses and surface context and a simple yet effective way of combining the two to perform disambiguation.Our approach improves on the languagedependent state of the art for two agglutinative languages (Turkish and Kazakh) and can be potentially applied to other morphologically complex languages.