Character-Aware Neural Morphological Disambiguation

Alymzhan Toleu, Gulmira Tolegen, Aibek Makazhanov


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.
Anthology ID:
P17-2105
Volume:
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
Month:
July
Year:
2017
Address:
Vancouver, Canada
Editors:
Regina Barzilay, Min-Yen Kan
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
666–671
Language:
URL:
https://aclanthology.org/P17-2105
DOI:
10.18653/v1/P17-2105
Bibkey:
Cite (ACL):
Alymzhan Toleu, Gulmira Tolegen, and Aibek Makazhanov. 2017. Character-Aware Neural Morphological Disambiguation. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 666–671, Vancouver, Canada. Association for Computational Linguistics.
Cite (Informal):
Character-Aware Neural Morphological Disambiguation (Toleu et al., ACL 2017)
Copy Citation:
PDF:
https://aclanthology.org/P17-2105.pdf