Using Semantic Knowledge in the Uyghur-Chinese Person Name Transliteration

Alim Murat, Turghun Osman, Yating Yang, Xi Zhou, Lei Wang and Xiao Li
Volume: 13, No: 4, Page: 716 ~ 730, Year: 2017
10.3745/JIPS.02.0065
Keywords: Gender, Language Origin, Semantic Knowledge-based Model, Transliteration of Person Name
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Abstract
In this paper, we propose a transliteration approach based on semantic information (i.e., language origin and gender) which are automatically learnt from the person name, aiming to transliterate the person name of Uyghur into Chinese. The proposed approach integrates semantic scores (i.e., performance on language origin and gender detection) with general transliteration model and generates the semantic knowledge-based model which can produce the best candidate transliteration results. In the experiment, we use the datasets which contain the person names of different language origins: Uyghur and Chinese. The results show that the proposed semantic transliteration model substantially outperforms the general transliteration model and greatly improves the mean reciprocal rank (MRR) performance on two datasets, as well as aids in developing more efficient transliteration for named entities.

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Cite this article
IEEE Style
Alim Murat, Turghun Osman, Yating Yang, Xi Zhou, Lei Wang and Xiao Li, "Using Semantic Knowledge in the Uyghur-Chinese Person Name Transliteration," Journal of Information Processing Systems, vol. 13, no. 4, pp. 716~730, 2017. DOI: 10.3745/JIPS.02.0065.

ACM Style
Alim Murat, Turghun Osman, Yating Yang, Xi Zhou, Lei Wang and Xiao Li, "Using Semantic Knowledge in the Uyghur-Chinese Person Name Transliteration," Journal of Information Processing Systems, 13, 4, (2017), 716~730. DOI: 10.3745/JIPS.02.0065.