Skip to main content
Springer Nature Link
Log in

Learning Chinese word embeddings from semantic and phonetic components

  • 1221: Deep Learning for Image/Video Compression and Visual Quality Assessment
  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

As an important task in Asian language information processing, Chinese word embedding learning has attracted much attention recently. Based on either Skip-gram or CBOW, several methods have been proposed to exploit Chinese characters and sub-character components for learning Chinese word embeddings. Chinese characters are combinations of meaning, structure, and phonetic information (pinyin). However, previous works only cover the former two aspects and cannot effectively explore distinct semantics of characters. To address this issue, we develop a Pinyin-enhance Skip-gram model named rsp2vec, in addition to a radical and pinyin-enhanced Chinese word embedding (rPCWE) learning models based on CBOW. For our models, the phonetic information and semantic components of Chinese characters are encoded into embeddings simultaneously. Evaluations on word analogy reasoning, word relevance, text classification, named entity recognition, and case studies validate the effectiveness of our models.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Algorithm 1
Fig. 3
Algorithm 2
Fig. 4

Similar content being viewed by others

Notes

  1. https://en.wikipedia.org/wiki/Chinese_language

  2. https://en.wikipedia.org/wiki/Chinese_characters

  3. https://en.wikipedia.org/wiki/Morpheme

  4. https://en.wikipedia.org/wiki/Pinyin#Tones

  5. https://en.wikipedia.org/wiki/Kangxi_Dictionary

  6. https://en.wikipedia.org/wiki/Radical_(Chinese_character)

  7. Our code is publicly available at https://github.com/luyy9apples/PictophoneticZhEmb.

  8. http://xh.5156edu.com/page/z9907m3552j18976.html

  9. https://radimrehurek.com/gensim/

  10. https://opencc.byvoid.com/

  11. https://github.com/fxsjy/jieba

  12. https://en.wikipedia.org/wiki/Courtesy_name

References

  1. Baroni M, Dinu G, Kruszewski G (2014) In: Proceedings of the 52nd annual meeting of the association for computational linguistics, pp 238–247

  2. Bengio Y, Ducharme R, Vincent P, Jauvin C (2003) A neural probabilistic language model. J Mach Learn Res 3:1137

    MATH  Google Scholar 

  3. Cao S, Lu W, Zhou J, Li X (2018) In: Proceedings of the 32nd AAAI conference on artificial intelligence, pp 5053–5061

  4. Chen X, Xu L, Liu Z, Sun M, Luan H (2015) In: Proceedings of the 24th international joint conference on artificial intelligence, pp 1236–1242

  5. Chen HY, Yu SH, Lin SD (2020) In: Proceedings of the 58th annual meeting of the association for computational linguistics, pp 2865–2871

  6. Fan RE, Chang KW, Hsieh CJ, Wang XR, Lin CJ (2008) Liblinear: a library for large linear classification. J Mach Learn Res 9:1871

    MATH  Google Scholar 

  7. Huang Z, Xu W, Yu K (2015) arXiv:1508.01991

  8. Li Y, Li W, Sun F, Li S (2015) In: Proceedings of the 2015 conference on empirical methods in natural language processing, pp 829–834

  9. Li H, Liu J, Liu RW, Xiong N, Wu K, hoon Kim T (2017) A dimensionality reduction-based multi-step clustering method for robust vessel trajectory analysis. Sensors 17(8):1792:1

    Article  Google Scholar 

  10. Ma B, Qi Q, Liao J, Sun H, Wang J (2020) Learning chinese word embeddings from character structural information. Comput Speech Language 60:101031

    Article  Google Scholar 

  11. Mikolov T, Sutskever I, Chen K, Corrado GS, Dean J (2013) In: Proceedings of the 27th annual conference on neural information processing systems, pp 3111–3119

  12. Mikolov T, Chen K, Corrado G, Dean J (2013) In: Proceedings of the 1st international conference on learning representations

  13. Pennington J, Socher R, Manning CD (2014) In: Proceedings of the 2014 conference on empirical methods in natural language processing, pp 1532–1543

  14. Rubenstein H, Goodenough JB (1965) Contextual correlates of synonymy. Commun ACM 8(10):627

    Article  Google Scholar 

  15. Schnabel T, Labutov I, Mimno D, Joachims T (2015) In: Proceedings of the 2015 conference on empirical methods in natural language processing, pp 298–307

  16. Shi X, Zhai J, Yang X, Xie Z, Liu C (2015) In: Proceedings of the 53rd annual meeting of the association for computational linguistics and the 7th international joint conference on natural language processing, pp 594–598

  17. Su TR, Lee HY (2017) In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pp 264–273

  18. Sun Z, Li X, Sun X, Meng Y, Ao X, He Q, Wu F, Li J (2021) In: Proceedings of the 59th annual meeting of the association for computational linguistics and the 11th international joint conference on natural language processing, pp 2065–2075

  19. Tang D, Wei F, Yang N, Zhou M, Liu T, Qin B (2014) In: Proceedings of the 52nd annual meeting of the association for computational linguistics, pp 1555–1565

  20. Wang S, Zhou W, Zhou Q (2020) Radical and stroke-enhanced chinese word embeddings based on neural networks. Neural Process Lett 52(2):1109

    Article  Google Scholar 

  21. Wu M, Tan L, Xiong N (2015) A structure fidelity approach for big data collection in wireless sensor networks. Sensors 15(1):248

    Article  Google Scholar 

  22. Yang Q, Xie H, Cheng G, Wang FL, Rao Y (2021) Pronunciation-enhanced chinese word embedding. Cogn Comput 2021. https://doi.org/10.1007/s12559-021-09850-9

  23. Yang L, Sun M (2015) In: Proceedings of the 14th China national conference on chinese computational linguistics and natural language processing based on naturally annotated big data, pp 15–25

  24. Yin R, Wang Q, Li P, Li R, Wang B (2016) In: Proceedings of the 2016 conference on empirical methods in natural language processing, pp 981–986

  25. Yu J, Jian X, Xin H, Song Y (2017) In: Proceedings of the 2017 conference on empirical methods in natural language processing, pp 286–291

  26. Zhang Y, Liu Y, Zhu J, Zheng Z, Liu X, Wang W, Chen Z, Zhai S (2019) Inproceedings of the 28th ACM international conference on information and knowledge management, pp 1011–1020

  27. Zeng Y, Sreenan CJ, Sitanayah L, Xiong N, Park JH, Zheng G (2011) An emergency- adaptive routing scheme for wireless sensor networks for building fire hazard monitoring. Sensors 11(3):2899

    Article  Google Scholar 

Download references

Acknowledgements

The work described in this paper was fully supported by a grant from the Research Grants Council of the Hong Kong Special Administrative Region, China (UGC/ FDS16/E01/19), the One-off Special Fund from Central and Faculty Fund in Support of Research from 2019/20 to 2021/22 (MIT02/19-20), the Research Cluster Fund (RG 78/2019-2020R), the Interdisciplinary Research Scheme of the Dean’s Research Fund 2019-20 (FLASS/ DRF/IDS-2) of The Education University of Hong Kong, and the Lam Woo Research Fund (LWI20011) of Lingnan University, Hong Kong.

Author information

Authors and Affiliations

  1. School of Science and Technology, Hong Kong Metropolitan University, Ho Man Tin, Hong Kong

    Fu Lee Wang

  2. School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China

    Yuyin Lu & Yanghui Rao

  3. Department of Mathematics and Information Technology, The Education University of Hong Kong, Tai Po, Hong Kong

    Gary Cheng

  4. Department of Computing and Decision Sciences, Lingnan University, Tuen Mun, Hong Kong

    Haoran Xie

Authors
  1. Fu Lee Wang
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Yuyin Lu
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Gary Cheng
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Haoran Xie
    View author publications

    You can also search for this author inPubMed Google Scholar

  5. Yanghui Rao
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Gary Cheng.

Ethics declarations

Conflict of Interests

The authors declare that they have no conflict of interest.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, F.L., Lu, Y., Cheng, G. et al. Learning Chinese word embeddings from semantic and phonetic components. Multimed Tools Appl 81, 42805–42820 (2022). https://doi.org/10.1007/s11042-022-13488-6

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-022-13488-6

Keywords