Character-Aware Neural Morphological Disambiguation

Alymzhan Toleu, Gulmira Tolegen, Aibek Makazhanov

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Citations (Scopus)


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 languageUndefined/Unknown
Title of host publicationProceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
Place of PublicationVancouver, Canada
PublisherAssociation for Computational Linguistics (ACL)
Number of pages6
Publication statusPublished - Jul 1 2017

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