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Term Translation Extraction from Historical Classics Using Modern Chinese Explanation

  • Xiaoting Wu
  • Hanyu Zhao
  • Chao Che
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11221)

Abstract

Extracting term translation pairs is of great help for Chinese historical classics translation since term translation is the most time-consuming and challenging part in the translation of historical classics. However, it is tough to recognize the terms directly from ancient Chinese due to the flexible syntactic of ancient Chinese and the word segmentation errors of ancient Chinese will lead to more errors in term translation extraction. Considering most of the terms in ancient Chinese are still reserved in modern Chinese and the terms in modern Chinese are more easily to be identified, we propose a term translation extracting method using multi-features based on character-based model to extract historical term translation pairs from modern Chinese-English corpora instead of ancient Chinese-English corpora. Specifically, we first employ character-based BiLSTM-CRF model to identify historical terms in modern Chinese without word segmentation, which avoids word segmentation error spreading to the term alignment. Then we extract English terms according to initial capitalization rules. At last, we align the English and Chinese terms based on co-occurrence frequency and transliteration feature. The experiment on Shiji demonstrates that the performance of the proposed method is far superior to the traditional method, which confirms the effectiveness of using modern Chinese as a substitute.

Keywords

BiLSTM-CRF Co-occurrence frequency Transliteration features Term translation extraction 

Notes

Acknowledgements

This work is supported by the National Natural Science Foundation of China (No. 61402068) and Support Program of Outstanding Young Scholar in Liaoning Universities (No. LJQ2015004).

References

  1. 1.
    Huang, Z.X.: English translation of cultural classics and postgraduate teaching of translation in Suzhou university. Shanghai J. Trans. 1, 56–58 (2007). (in Chinese)Google Scholar
  2. 2.
    Wang, B.: Translation pairs extraction from unaligned Chinese-English bilingual corpora. J. Chin. Inf. Process. 14(6), 40–44 (2000). (in Chinese)MathSciNetGoogle Scholar
  3. 3.
    Yang, P., Hou, H.X., Jiang, Y.P., Jian, Shen, Z., D.U.: Chinese-Slavic Mongolian named entity translation based on word alignment. Acta Scientiarum Naturalium Universitatis Pekinensis 52(1), 148–154 (2016). (in Chinese)Google Scholar
  4. 4.
    Lample, G., Ballesteros, M., Subramanian, S., Kawakami, K., Dyer, C.: Neural Architectures for Named Entity Recognition, pp. 260–270 (2016)Google Scholar
  5. 5.
    Zeng, D., Sun, C., Lin, L., Liu, B.: LSTM-CRF for drug-named entity recognition. Entropy 19(6), 283 (2017)CrossRefGoogle Scholar
  6. 6.
    Dong, C., Zhang, J., Zong, C., Hattori, M., Di, H.: Character-based LSTM-CRF with radical-level features for Chinese named entity recognition. In: Lin, C.-Y., Xue, N., Zhao, D., Huang, X., Feng, Y. (eds.) ICCPOL/NLPCC-2016. LNCS (LNAI), vol. 10102, pp. 239–250. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-50496-4_20CrossRefGoogle Scholar
  7. 7.
    Li, X., Che, C., Liu, X., Lin, H., Wang, R.: Corpus-based extraction of Chinese historical term translation equivalents. Int. J. of Asian Lang. Proc. 20(2), 63–74 (2010)Google Scholar
  8. 8.
    Zhou, K.: Research on named entity recognition based on rules. Hefei University of Technology (2010). (in Chinese)Google Scholar
  9. 9.
    Hai, L.C., Ng, H.T: Named entity recognition: a maximum entropy approach using global information. In: International Conference on Computational Linguistics (2002)Google Scholar
  10. 10.
    Li, L., Mao, T., Huang, D., Tang, Y.: Hybrid models for chinese named entity recognition. In: Proceedings of the Fifth Sighan Workshop on Chinese Language Processing, pp. 72–78 (2006)Google Scholar
  11. 11.
    Şeker, G.A., Eryiğit, G.: Extending a CRF-based named entity recognition model for Turkish well formed text and user generated content 1. Semant. Web 8(5), 1–18 (2017)CrossRefGoogle Scholar
  12. 12.
    Sun, L., Guo, Y., Tang, W., et al.: Enterprise abbreviation prediction based on constitution pattern and conditional random field. J. Comput. Appl. 36(2), 449–454 (2016)Google Scholar
  13. 13.
    Patil, N.V., Patil, A.S., Pawar, B.V.: HMM based named entity recognition for inflectional language. In: International Conference on Computer, Communications and Electronics (2017)Google Scholar
  14. 14.
    Wang, G.Y.: Research of chinese named entity recognition based on deep learning. Beijing University of Technology (2015). (in Chinese)Google Scholar
  15. 15.
    Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: LSTM: a search space odyssey. IEEE Trans. Neural Netw. Learn. Syst. 28(10), 2222–2232 (2017)MathSciNetCrossRefGoogle Scholar
  16. 16.
    Settles, B., Craven, M.: An analysis of active learning strategies for sequence labeling tasks. In: Conference on Empirical Methods in Natural Language Processing (2008)Google Scholar
  17. 17.
    Watson, B.: Record of the grand historian of China. J. Asian Stud. 22(2), 205 (1961)Google Scholar
  18. 18.
    Che, C., Zheng, X.J.: Sub-word based translation extraction for terms in Chinese historical classics. J. Chin. Inf. Process. 30(3), 46–51 (2016). (in Chinese)Google Scholar
  19. 19.
    Sixty professors in Taiwan’s 14 institutions. The Chronicle of the Vernacular History. New World Press (2007). (in Chinese)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Key Laboratory of Advanced Design and Intelligent Computing, Ministry of EducationDalian UniversityDalianChina

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