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A bilingual word alignment algorithm of Vietnamese-Chinese based on feature constraint

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Abstract

It is difficult to achieve auto-alignment between Vietnamese and Chinese, because their syntax and structure are quite different. In this case we present a novel method for the Vietnamese-Chinese word alignment which merges a variety of feature constraint models. In this article, an improved model based on the Vietnamese-Chinese progressive structure and offset features of word sequence is described. From this model which is trained by a log-linear model framework, and with parameters trained by the minimum error rate algorithm, the result of the Vietnamese-Chinese auto-alignment is obtained. The basic model of the experiments is IBM Model 3, and as experimental results suggest, this bilingual word alignment method for Vietnamese and Chinese performs well and precision, recall rates are increased by 28.57 and 25.02 %, AER is reduced by 14.25 %.

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Acknowledgments

This paper is supported by the National Nature Science Foundation of China (No. 61262041, No. 61175068, No. 61163022), and the Open Fund of Software Engineering Key Laboratory of Yunnan Province (No. 2011SE14), and the key project of Yunnan Nature Science Foundation (No. 2013FA030), and the Ministry of Education of Returned Overseas Students to Start Research and Fund Projects.

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Authors and Affiliations

  1. School of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, 650500, China

    Yuanyuan Mo, Jianyi Guo, Zhengtao Yu, Lin Luo & Shengxiang Gao

  2. Key Lab of Intelligent Information Processing, Kunming University of Science and Technology, Kunming, 650500, China

    Jianyi Guo & Zhengtao Yu

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  1. Yuanyuan Mo
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  2. Jianyi Guo
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  3. Zhengtao Yu
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  4. Lin Luo
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  5. Shengxiang Gao
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Correspondence to Zhengtao Yu.

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Mo, Y., Guo, J., Yu, Z. et al. A bilingual word alignment algorithm of Vietnamese-Chinese based on feature constraint. Int. J. Mach. Learn. & Cyber. 6, 537–543 (2015). https://doi.org/10.1007/s13042-014-0293-6

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  • DOI: https://doi.org/10.1007/s13042-014-0293-6

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