Abstract
For low-resource languages like Uyghur, data sparseness is always a serious problem in related information processing, especially in some tasks based on parallel texts. To enrich bilingual resources, we detect Chinese and Russian loan words from Uyghur texts according to phonetic similarities between a loan word and its corresponding donor language word. In this paper, we propose a novel approach based on perceptron model to discover loan words from Uyghur texts, which consider the detection of loan words in Uyghur as a classification procedure. The experimental results show that our method is capable of detecting the Chinese and Russian loan words in Uyghur Texts effectively.
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References
Chris, M., Hinrich, S.: Foundations of Statistical Natural Language Processing. MIT Press, Cambridge (1999)
Chung, C., Ho, C., Ping, C.: Using Sublexical Translations to Handle the OOV Problem in Machine Translation. ACM Transactions on Asian Language Information Processing 10(3), 1–20 (2011)
Lauren, A.H.L.: English Loanwords in Mandarin Chinese. The University of Arizona, Arizona (2002)
Gillian, K.: English loanwords in Japanese. World Englishes 14(1), 67–76 (1995)
Kui, Z.: On Chinese-English Language Contact through Loanwords. English Language and Literature Studies 1(2), 100–105 (2011)
Xuan, L., Lanqin, Z.: On Chinese Loanwords in English. Theory and Practice in Language Studies 1(12), 1816–1819 (2011)
Yan, C., Ping, C.: A Comparison on the methods of Uyghur and Chinese Loan Words. Journal of Kashgar Teachers College 32(2), 51–55 (2011)
Yan, Z.: Influence of Loan Words on the Words of Uygur Language. Journal of Hubei University of Education 28(1), 37–39 (2011)
Shiming, C.: New Research on Chinese Loan Words in the Uygur Language. N.W.Journal of Ethnology 28(1), 176–180 (2011)
Mi, C., Yang, Y., Zhou, X., Li, X., Yang, M.: Recognition of Chinese Loan Words in Uyghur Based on String Similarity. Journal of Chinese Information Processing 27(5), 173–178 (2013)
Brown, P.E., Pietra, S.A.D., Pietra, V.J.D., Mercer, R.L.: The mathematics of statistical machine translation: Parameter estimation. Computational Linguistics 19(2), 263–311 (1993)
Vogel, S., Ney, H., Tillmann, C.: Hmm-based word alignment in statistical translation. In: Proceedings of the 16th Conference on Computational Linguistics, pp. 836–841. Association for Computational Linguistics (1996)
Dempster, A., Laird, N., Rubin, D.B.: Maximum-likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society. Series B (Methodological) (39), 1–38 (1977)
Gallant, S.I.: Perceptron-based learning algorithms. IEEE Transactions on Neural Networks 1(2), 179–191 (1990)
Collins, M.: Discriminative training methods for hidden markov models: Theory and experiments with perceptron algorithms. In: Proceedings of the ACL 2002 Conference on Empirical Methods in Natural Language Processing, vol. 10. Association for Computational Linguistics (2002)
Dasgupta, S., Kalai, A.T., Monteleoni, C.: Analysis of perceptron-based active learning. Learning Theory, 249–263 (2005)
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Mi, C., Yang, Y., Wang, L., Li, X., Dalielihan, K. (2014). Detection of Loan Words in Uyghur Texts. In: Zong, C., Nie, JY., Zhao, D., Feng, Y. (eds) Natural Language Processing and Chinese Computing. NLPCC 2014. Communications in Computer and Information Science, vol 496. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45924-9_10
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DOI: https://doi.org/10.1007/978-3-662-45924-9_10
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