Abstract
While distributed representations have proven to be very successful in a variety of NLP tasks, learning distributed representations for agglutinative languages such as Uyghur still faces a major challenge: most words are composed of many morphemes and occur only once on the training data. To address the data sparsity problem, we propose an approach to learn distributed representations of Uyghur words and morphemes from unlabeled data. The central idea is to treat morphemes rather than words as the basic unit of representation learning. We annotate a Uyghur word similarity dataset and show that our approach achieves significant improvements over CBOW, a state-of-the-art model for computing vector representations of words.
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Acknowledgments
This research is supported by National Key Basic Research Program of China (973 Program 2014CB340500), the National Natural Science Foundation of China (No. 61331013), the National Key Technology R & D Program (No. 2014BAK10B03), the Singapore National Research Foundation under its International Research Center @ Singapore Funding Initiative and administered by the IDM Programme. We are grateful to Meiping Dong, Lei Xu, Liner Yang, Yu Zhao, Yankai Lin, Chunyang Liu, Shiqi Shen, and Meng Zhang for their constructive feedback to the early draft of this paper.
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Abudukelimu, H., Liu, Y., Chen, X., Sun, M., Abulizi, A. (2015). Learning Distributed Representations of Uyghur Words and Morphemes. In: Sun, M., Liu, Z., Zhang, M., Liu, Y. (eds) Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data. CCL NLP-NABD 2015 2015. Lecture Notes in Computer Science(), vol 9427. Springer, Cham. https://doi.org/10.1007/978-3-319-25816-4_17
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DOI: https://doi.org/10.1007/978-3-319-25816-4_17
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