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Linguistic Annotation of Translated Chinese Texts: Coordinating Theory, Algorithms and Data

Journal of Linguistics/Jazykovedný casopis
Volume 72 (2021): Issue 2 (December 2021)
By:
Open Access
|Dec 2021

References

  1. [1] Semenov, K. I., Kuznetsova, Y. N., and Durneva, S. P. (2020). Russian-Chinese parallel corpus of RNC: Problems and perspectives. Proceedings of the 10th International Conference “Russia and China: History and Perspectives for Cooperation”, pages 633–640.
    Search in Google ScholarBack to article
  2. [2] Emerson, T. (2005). The Second International Chinese Word Segmentation Bakeoff. Accessible at: http://sighan.cs.uchicago.edu/bakeoff2005/.
    Search in Google ScholarBack to article
  3. [3] Li, P.-H., and Ma, W.-Y. (2019). CkipTagger. Accessible at: https://github.com/ckiplab/ckiptagger.
    Search in Google ScholarBack to article
  4. [4] Qi, P., Zhang, Y., Zhang, Y., Bolton, J., and Manning, C. D. (2020). Stanza: A Python Natural Language Processing Toolkit for Many Human Languages. Association for Computational Linguistics (ACL) System Demonstrations. Accessible at: https://nlp.stanford.edu/pubs/qi2020stanza.pdf.
    Search in Google ScholarBack to article
  5. [5] Honnibal, M., and Montani, I. (2017). spaCy 2: Natural language understanding with Bloom embeddings, convolutional neural networks and incremental parsing. Accessible at: https://spacy.io/.
    Search in Google ScholarBack to article
  6. [6] Luo, R., xu, J., Zhang, Y., Ren, x., and Sun, x. (2019). PKUSEG: A Toolkit for Multi-Domain Chinese Word Segmentation. Accessible at: http://arxiv.org/abs/1906.11455.
    Search in Google ScholarBack to article
  7. [7] Geng, Z., Yan, H., Qiu, x., and Huang, x. (2020). fastHan: A BERT-based Joint Many-Task Toolkit for Chinese NLP. Accessible at: http://arxiv.org/abs/2009.08633.
    Search in Google ScholarBack to article
  8. [8] Zhang, H., and Shang, J. (2019). NLPIR-Parser: An intelligent semantic analysis toolkit for big data. Corpus Linguistics, 6(1), pages 87–104.
    Search in Google ScholarBack to article
  9. [9] Che, W., Feng, Y., Qin, L., and Liu, T. (2021). N-LTP: A Open-source Neural Chinese Language Technology Platform with Pretrained Models. Accessible at: http://arxiv.org/abs/2009.11616.
    Search in Google ScholarBack to article
  10. [10] Straka, M. (2018). UDPipe 2.0 Prototype at CoNLL 2018 UD Shared Task. Proceedings of the CoNLL 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies, pages 197–207. Accessible at: https://doi.org/10.18653/v1/K18-2020.10.18653/v1/K18-2020
    Search in Google ScholarBack to article
  11. [11] Semenov, K. I., Korotkova, Y. O., Volf, E. A., and Konovalova, A. S. (2021). Automatic Annotation of the Chinese Texts that Contain Loanwords: Word Segmentation, Transcription, PoS-tagging. DIALOG-2021: 27th International Conference on Computational Linguistics and Intellectual Technologies, Supplementary volume, pages 1081–1095. Accessible at: http://www.dialog-21.ru/media/5420/_-dialog2021supvol.pdf.
    Search in Google ScholarBack to article
  12. [12] Cai, Z., Yang, Y., Zhang, C., Qin, x., and Li, M. (2019). Polyphone Disambiguation for Mandarin Chinese Using Conditional Neural Network with Multi-level Embedding Features. Accessible at: https://arxiv.org/abs/1907.01749.
    Search in Google ScholarBack to article
  13. [13] Park, K., and Lee, S. (2020). g2pM: A Neural Grapheme-to-Phoneme Conversion Package for Mandarin Chinese Based on a New Open Benchmark Dataset. Accessible at: http://arxiv.org/abs/2004.03136.
    Search in Google ScholarBack to article
  14. [14] Luo, E. (2020). xpinyin. Accessible at: https://github.com/lxneng/xpinyin.
    Search in Google ScholarBack to article
  15. [15] Huang, H. (2020). pypinyin. Accessible at: https://github.com/mozillazg/python-pinyin.
    Search in Google ScholarBack to article
  16. [16] Konovalova, A. S., and Tsvetkova, A. D. (2021). Comparative analysis of grapheme-to-phoneme models for the Russian-Chinese parallel corpus. Program book of Buckeye East Asian Linguistics Forum 4, pages 28–30. Accessible at: https://cpb-us-w2.wpmucdn.com/u.osu.edu/dist/6/3609/files/2021/03/BEALF-4_Program_Book_2021-3-5.pdf.
    Search in Google ScholarBack to article
  17. [17] Roten, T. S. (2018). PyNLPIR PoS tagset. Accessible at: https://pynlpir.readthedocs.io/en/latest/api.html.
    Search in Google ScholarBack to article
  18. [18] Semenov, K. I., Korotkova, Y. O., and Volf, E. A. (2021). Automatic Annotation of the Russian Loanwords in Chinese Texts: Issues in Word Segmentation and PoS-tagging. Proceedings of Corpora 2021 International Conference. 14 pages [in press].
    Search in Google ScholarBack to article
  19. [19] Konovalova, A. S. (2021). Automatic POS-tagging for Chinese Using Parallel Data [BA thesis]. Higher School of Economics. 82 pages.
    Search in Google ScholarBack to article
DOI: https://doi.org/10.2478/jazcas-2021-0054 | Journal eISSN: 1338-4287 | Journal ISSN: 0021-5597
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Language: English
Page range: 590 - 602
Published on: Dec 30, 2021
Published by: Slovak Academy of Sciences, Mathematical Institute
In partnership with: Paradigm Publishing Services
Publication frequency: 2 issues per year
Keywords:
Mandarin,
Russian,
parallel corpus,
Chinese word segmentation (CWS),
grapheme-to-phoneme conversion (G2P),
PoS-tagging,
code-switching detection
Related subjects:
Linguistics and semiotics,
Theoretical frameworks and disciplines,
Linguistics, other

© 2021 Kirill I. Semenov, Armine K. Titizian, Aleksandra O. Piskunova, Yulia O. Korotkova, Alena D. Tsvetkova, Elena A. Volf, Alexandra S. Konovalova, Yulia N. Kuznetsova, published by Slovak Academy of Sciences, Mathematical Institute
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

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