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Optimized Uyghur Segmentation for Statistical Machine Translation

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Natural Language Processing and Information Systems (NLDB 2015)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9103))

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Abstract

In this paper, we propose an optimized method to segment the Uyghur word. We consider the optimization as a classification problem; the features are extracted from Uyghur-Chinese bilingual corpus. Experimental results show that with our method the performance of Uyghur-Chinese machine translation improved significantly.

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References

  1. Larkey, L.S., Ballesteros, L., Connell, M. E.: Improving stemming for Arabic information retrieval: light stemming and co-occurrence analysis. In: Proceedings of the 25th ACM SIGIR, pp. 275–282 (2002)

    Google Scholar 

  2. Nguyen, T.L., Vogel, S., Smith, N.A.: Nonparametric word segmentation for machine translation. In: Proceedings of the 23rd COLING, pp. 815–823 (2010)

    Google Scholar 

  3. Lafferty, J., McCallum, A., Pereira, F.C.N.: Conditional random fields: Probabilistic models for segmenting and labeling sequence data. In: Proceedings of the 8th ICML, pp. 282–289 (2001)

    Google Scholar 

  4. McDonald, J.H: Handbook of Biological Statistics. 2nd edn pp. 173–181 (2009)

    Google Scholar 

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Acknowledgements

This work is supported by the National High Technology Research and Development Program of China (No. 2013AA01A607), Strategic Priority Research Program of the Chinese Academy of Sciences (No. XDA06030400), West Light Foundation of Chinese Academy of Sciences (No. XBBS201216), and Key Project of Knowledge Innovation Program of Chinese Academy of Sciences (No. KGZD-EW-501).

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Authors and Affiliations

  1. Xinjiang Technical Institute of Physics and Chemistry of Chinese Academy of Sciences Urumqi, Xinjiang, 830011, China

    Chenggang Mi, Yating Yang, Rui Dong, Xi Zhou, Lei Wang, Xiao Li, Tonghai Jiang & Turghun Osman

  2. University of Chinese Academy of Sciences Beijing, Beijing, 100049, China

    Chenggang Mi & Rui Dong

Authors
  1. Chenggang Mi
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  2. Yating Yang
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  3. Rui Dong
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  4. Xi Zhou
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  5. Lei Wang
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  6. Xiao Li
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  7. Tonghai Jiang
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  8. Turghun Osman
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Corresponding author

Correspondence to Chenggang Mi .

Editor information

Editors and Affiliations

  1. Technische Universität Darmstadt, Darmstadt, Germany

    Chris Biemann

  2. Universität Passau, Passau, Germany

    Siegfried Handschuh

  3. Universität Passau, Passau, Germany

    André Freitas

  4. University of Salford, Salford, United Kingdom

    Farid Meziane

  5. Conservatoire National des Arts et Métiers, Paris, France

    Elisabeth Métais

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© 2015 Springer International Publishing Switzerland

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Mi, C. et al. (2015). Optimized Uyghur Segmentation for Statistical Machine Translation. In: Biemann, C., Handschuh, S., Freitas, A., Meziane, F., Métais, E. (eds) Natural Language Processing and Information Systems. NLDB 2015. Lecture Notes in Computer Science(), vol 9103. Springer, Cham. https://doi.org/10.1007/978-3-319-19581-0_36

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  • DOI: https://doi.org/10.1007/978-3-319-19581-0_36

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-19580-3

  • Online ISBN: 978-3-319-19581-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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