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 language-dependent state of the art for two agglutinative languages (Turkish and Kazakh) and can be potentially applied to other morphologically complex languages.
|Title of host publication||Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)|
|Place of Publication||Vancouver, Canada|
|Publisher||Association for Computational Linguistics (ACL)|
|Number of pages||6|
|Publication status||Published - Jul 1 2017|