Research advanced in Chinese word segmentation methods and challenges

Research Article
Open access

Research advanced in Chinese word segmentation methods and challenges

Guancheng Du 1*
  • 1 Beijing No.18 High School    
  • *corresponding author shuoren100@163.com
Published on 7 February 2024 | https://doi.org/10.54254/2755-2721/37/20230464
ACE Vol.37
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-83558-299-2
ISBN (Online): 978-1-83558-300-5

Abstract

Chinese word segmentation refers to the process of dividing a sequence of Chinese characters into individual words. It constitutes a fundamental component of Chinese natural language processing. Due to the intricacies of the Chinese language, Chinese word segmentation has garnered significant attention from researchers. Based on a review of historical literature, segmentation methods can be broadly categorized into rule-based, statistical, semantic-based, and comprehension-based approaches. With the advancement of machine learning, neural networks have emerged as the mainstream algorithm for word segmentation. However, Chinese presents several unique challenges, leading to segmentation results that are less effective compared to morphological analysis in languages like English. Moreover, word segmentation faces new challenges such as dependency on the quality and scale of corpora, as well as domain-specific segmentation in diverse fields. Addressing these emerging challenges will undoubtedly become a focal point in future research endeavors in this field. This review provides a comprehensive summary of existing methods, discusses the current state of Chinese word segmentation, and outlines directions for addressing the evolving complexities in the field. As Chinese language processing continues to advance, finding robust solutions for accurate word segmentation remains a critical area of research.

Keywords:

Chinese word segmentation, Natural Language Processing, neural networks, cross-domain word segmentation

Du,G. (2024). Research advanced in Chinese word segmentation methods and challenges. Applied and Computational Engineering,37,16-22.
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References

[1]. LI, X., MENG, Y., SUN, X., et al. Is word segmentation necessary for deep learning of Chinese representations? In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, 2019, pp. 359-401.

[2]. CHEN, L., ZHAO, Y., LYU, B., et al. Neural graph matching networks for Chinese short text matching. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, 2020, pp. 6152-6158.

[3]. YANG, J. X., DU, J. P., SHAO, Y. X., et al. Construction method of intellectual-property-oriented scientific and technological resources portrait. Journal of Software, 2022, 33(4): 1439-1450.

[4]. Luo, R., Xu, J., Zhang, Y., Zhang, Z., Ren, X., & Sun, X. (2019). Pkuseg: A toolkit for multi-domain chinese word segmentation. arXiv preprint arXiv:1906.11455.

[5]. Li, P., Luo, A., Liu, J., Wang, Y., Zhu, J., Deng, Y., & Zhang, J. (2020). Bidirectional gated recurrent unit neural network for Chinese address element segmentation. ISPRS International Journal of Geo-Information, 9(11), 635.

[6]. Yan, X., Xiong, X., Cheng, X., Huang, Y., Zhu, H., & Hu, F. (2021). HMM-BiMM: Hidden Markov Model-based word segmentation via improved Bi-directional Maximal Matching algorithm. Computers & Electrical Engineering, 94, 107354.

[7]. Brouwer, H., Delogu, F., Venhuizen, N. J., & Crocker, M. W. (2021). Neurobehavioral correlates of surprisal in language comprehension: A neurocomputational model. Frontiers in Psychology, 12, 615538.

[8]. Tian, X., & Jia, W. (2022). Optimal matching method based on rare plants in opportunistic social networks. Journal of Computational Science, 64, 101875.

[9]. Baomao, P., & Haoshan, S. (2009, August). Research on improved algorithm for Chinese word segmentation based on Markov chain. In 2009 Fifth International Conference on Information Assurance and Security (Vol. 1, pp. 236-238). IEEE.

[10]. Novak, J. R., Minematsu, N., & Hirose, K. (2016). Phonetisaurus: Exploring grapheme-to-phoneme conversion with joint n-gram models in the WFST framework. Natural Language Engineering, 22(6), 907-938.

[11]. Mor, B., Garhwal, S., & Kumar, A. (2021). A systematic review of hidden Markov models and their applications. Archives of computational methods in engineering, 28, 1429-1448.

[12]. Yuan, H., & Ji, S. (2020, January). Structpool: Structured graph pooling via conditional random fields. In Proceedings of the 8th International Conference on Learning Representations.

Cite this article

Du,G. (2024). Research advanced in Chinese word segmentation methods and challenges. Applied and Computational Engineering,37,16-22.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 2023 International Conference on Machine Learning and Automation

ISBN：978-1-83558-299-2(Print) / 978-1-83558-300-5(Online)
Editor：Mustafa İSTANBULLU
Conference website: https://2023.confmla.org/
Conference date: 18 October 2023
Series: Applied and Computational Engineering
Volume number: Vol.37
ISSN：2755-2721(Print) / 2755-273X(Online)

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References

[1]. LI, X., MENG, Y., SUN, X., et al. Is word segmentation necessary for deep learning of Chinese representations? In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, 2019, pp. 359-401.

[2]. CHEN, L., ZHAO, Y., LYU, B., et al. Neural graph matching networks for Chinese short text matching. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, 2020, pp. 6152-6158.

[3]. YANG, J. X., DU, J. P., SHAO, Y. X., et al. Construction method of intellectual-property-oriented scientific and technological resources portrait. Journal of Software, 2022, 33(4): 1439-1450.

[4]. Luo, R., Xu, J., Zhang, Y., Zhang, Z., Ren, X., & Sun, X. (2019). Pkuseg: A toolkit for multi-domain chinese word segmentation. arXiv preprint arXiv:1906.11455.

[5]. Li, P., Luo, A., Liu, J., Wang, Y., Zhu, J., Deng, Y., & Zhang, J. (2020). Bidirectional gated recurrent unit neural network for Chinese address element segmentation. ISPRS International Journal of Geo-Information, 9(11), 635.

[6]. Yan, X., Xiong, X., Cheng, X., Huang, Y., Zhu, H., & Hu, F. (2021). HMM-BiMM: Hidden Markov Model-based word segmentation via improved Bi-directional Maximal Matching algorithm. Computers & Electrical Engineering, 94, 107354.

[7]. Brouwer, H., Delogu, F., Venhuizen, N. J., & Crocker, M. W. (2021). Neurobehavioral correlates of surprisal in language comprehension: A neurocomputational model. Frontiers in Psychology, 12, 615538.

[8]. Tian, X., & Jia, W. (2022). Optimal matching method based on rare plants in opportunistic social networks. Journal of Computational Science, 64, 101875.

[9]. Baomao, P., & Haoshan, S. (2009, August). Research on improved algorithm for Chinese word segmentation based on Markov chain. In 2009 Fifth International Conference on Information Assurance and Security (Vol. 1, pp. 236-238). IEEE.

[10]. Novak, J. R., Minematsu, N., & Hirose, K. (2016). Phonetisaurus: Exploring grapheme-to-phoneme conversion with joint n-gram models in the WFST framework. Natural Language Engineering, 22(6), 907-938.

[11]. Mor, B., Garhwal, S., & Kumar, A. (2021). A systematic review of hidden Markov models and their applications. Archives of computational methods in engineering, 28, 1429-1448.

[12]. Yuan, H., & Ji, S. (2020, January). Structpool: Structured graph pooling via conditional random fields. In Proceedings of the 8th International Conference on Learning Representations.

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