Tibetan Syllable-Based Functional Chunk Boundary Identification

  • Shumin ShiEmail author
  • Yujian Liu
  • Tianhang Wang
  • Congjun Long
  • Heyan Huang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10565)


Tibetan syntactic functional chunk parsing is aimed at identifying syntactic constituents of Tibetan sentences. In this paper, based on the Tibetan syntactic functional chunk description system, we propose a method which puts syllables in groups instead of word segmentation and tagging and use the Conditional Random Fields (CRFs) to identify the functional chunk boundary of a sentence. According to the actual characteristics of the Tibetan language, we firstly identify and extract the syntactic markers as identification characteristics of syntactic functional chunk boundary in the text preprocessing stage, while the syntactic markers are composed of the sticky written form and the non-sticky written form. Afterwards we identify the syntactic functional chunk boundary using CRF. Experiments have been performed on a Tibetan language corpus containing 46783 syllables and the precision, recall rate and F value respectively achieves 75.70%, 82.54% and 79.12%. The experiment results show that the proposed method is effective when applied to a small-scale unlabeled corpus and can provide foundational support for many natural language processing applications such as machine translation.


Tibetan syntactic functional chunk Chunk boundary recognition Syllable Syntactic marker CRF 



This work is supported by the National Natural Science Foundation of China (61671064, 61201352, and 61132009), the National Key Basic Research Program of China (2013CB329303) and the Fundamental Research Fund of Beijing Institute of Technology (20130742010).


  1. 1.
    Church, K.W.: A stochastic parts program and noun phrase parser for unrestricted text. In: Proceedings of the second Conference on Applied Natural Language Processing, pp. 136–143. Association for Computational Linguistics (1988)Google Scholar
  2. 2.
    Pla, F., Molina, A., Prieto, N.: An integrated statistical model for tagging and chunking unrestricted text. In: The Third International Workshop on Text, Speech and Dialogue, Brno, Czech Republic, pp. 15–20 (2000)Google Scholar
  3. 3.
    Sun, H.L.: Induction of grammatical rules from an annotated corpus V+N sequence analysis. In: China National Conference on Computational Linguistics, pp. 157–163. Tsinghua University Press, Beijing(1997)Google Scholar
  4. 4.
    Liu, C.Z.: Research on binding of common noun sequences based on POS tagging corpus. In: Proceedings of the International Conference on Chinese Information Processing, Tsinghua University Press, Beijing (1998)Google Scholar
  5. 5.
    Li, W.J., Zhou, M., et al.: Automatic extraction of Chinese longest noun phrases based on corpus. In: Chen, L., Yuan, Q. (eds.) Progress and Application of Computational Linguistics, pp. 119–124. Tsinghua University Press, Beijing (1995)Google Scholar
  6. 6.
    Huang, D., Yu, J.: The combination of distributed strategy and CRFs to identify Chinese chunk. J. Chinese Inf. Proces. 23(1), 16–22 (2009)Google Scholar
  7. 7.
    Dai, C., Zhou, Q.L., Cai, D.F., et al.: Automatic identification of Chinese maximum noun phrase based on statistics and rules. J. Chinese Inf. Proces. 22(6), 110–115 (2008)Google Scholar
  8. 8.
    Drábek, E.F., Zhou, Q.: Experiments in learning models for functional chunking of Chinese text. In: IEEE International Conference on Systems, Man, and Cybernetics IEEE, vol. 2, pp. 859–864 (2001)Google Scholar
  9. 9.
    Chen, Y., Zhou, Q.: Analysis and construction of hierarchical chinese function block description library. J. Chinese Inf. Proces. 22(3), 24–31 (2008)MathSciNetGoogle Scholar
  10. 10.
    Zhou, Q., Zhao, Y.Z.: Automatic parsing of chinese functional chunks. Chin. J. Inf. 21(5), 18–24 (2007)Google Scholar
  11. 11.
    Jiang, D., Kang, C.J.: The methods of lemmatization of bound case makers in modern Tibetan. In: International Conference on Natural Language Processing and Knowledge Engineering, pp. 616–621(2003)Google Scholar
  12. 12.
    Jiang, D.: The method and process of block segmentation in modern Tibetan. Minor. Lang. China 2003(4), 30–39 (2003)Google Scholar
  13. 13.
    Long, C.J., Kang, C.J., Jiang, D.: The comparative research on the segmentation strategies of tibetan bounded-variant forms. In: International Conference on Asian Language Processing 2013(30), pp. 243–246. IEEE Computer Society (2013)Google Scholar
  14. 14.
    Wang, T.H., Shi, S.H., Long, C.J., et al.: Syntactic boundary block identification of Tibetan syntactic functions based on error driven learning strategy. Chinese J. Inf. 28(5), 170–175 (2014)Google Scholar
  15. 15.
    Wang, T.H.: Research on Tibetan functional block recognition for Machine Translation. Beijing Institute of Technology (2016)Google Scholar
  16. 16.
    Liu, H.D.: Research on Tibetan Word Segmentation and Text Resource Mining. University of the Chinese Academy of Sciences (2012)Google Scholar
  17. 17.
    Li, Y.C., Jia, Y.J., Zong, C.Q.: Research and implementation of tibetan automatic word segmentation based on conditional random field. J. Chinese Inf. Proces. 27(4), 52–58 (2013)Google Scholar
  18. 18.
    Li, L., Long, C.J., Jiang, D.: Tibetan functional chunks boundary detection. J. Chinese Inf. Proces. 27(6), 165–168 (2013)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Shumin Shi
    • 1
    • 2
    Email author
  • Yujian Liu
    • 1
  • Tianhang Wang
    • 1
  • Congjun Long
    • 3
  • Heyan Huang
    • 1
    • 2
  1. 1.School of Computer Science and Technology Beijing Institute of TechnologyBeijingChina
  2. 2.Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing ApplicationsBeijingChina
  3. 3.Institute of Ethnology and Anthropology Chinese Academy of Social SciencesBeijingChina

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