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 language-dependent state of the art for two agglutinative languages (Turkish and Kazakh) and can be potentially applied to other morphologically complex languages.
Original language | Undefined/Unknown |
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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) |
Pages | 666-671 |
Number of pages | 6 |
Publication status | Published - Jul 1 2017 |