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International Conference on Intelligent Computing

ICIC 2022: Intelligent Computing Methodologies pp 170–182Cite as

An Effective Chinese Text Classification Method with Contextualized Weak Supervision for Review Autograding

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13395))

Abstract

This paper aims to develop a Chinese text classification workflow in education situations, where a grade can swing due to subjective cognitive loads. This problem is often observed between the academic paper comments and their grades, leading to a challenge in Chinese texts. To analyze this problem, we in this paper introduce an effective Chinese text classifier by extending the popular seed words-based model into an effective workflow. We first made texts into vectors in the proposed method using Chinese preprocessing. We then exploited the bidirectional encoder representations from Transformers to integrate the contextualization features, then performed a hierarchical attention network for classification. In this study, we collected 4,310 review comment short-texts involving 140 universities in China. As these texts include noisy grades from experts, the proposed method yields seed words for each category, resulting in pseudo labels to weakly supervise the network training instead of the noisy labels. We finally evaluated the designed workflow on the real-world datasets and achieved a good performance in Chinese classification compared with the traditional models. This study provides insights into a real educational text case where a review grade can swing due to subjective cognitive loads and an available workflow to automatically grade these Chinese expert comment texts, facilitating the precise academic evaluation system.

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Acknowledgement

This study was funded in part by the National Natural Science Foundation of China (U1811262, 61802313, 61772426), the Key Research and Development Program of China (2020AAA0108500), the Reformation Research on Education and Teaching at Northwestern Polytechnical University (2021JGY31), the Higher Research Funding on International Talent cultivation at Northwestern Polytechnical University (GJGZZD202202), Research Topic at The Chinese Society of Academic Degrees and Graduate Education (2020ZA1008).

Author information

Authors and Affiliations

  1. School of Computer Science, Northwestern Polytechnical University, Xi’an, China

    Yupei Zhang, Md Shahedul Islam Khan, Yaya Zhou & Xuequn Shang

  2. MIIT Big Data Storage and Management Libraries, Xi’an, China

    Yupei Zhang, Yaya Zhou & Xuequn Shang

  3. Graduate School, Northwestern Polytechnical University, Xi’an, China

    Min Xiao

Authors
  1. Yupei Zhang
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  2. Md Shahedul Islam Khan
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  3. Yaya Zhou
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  4. Min Xiao
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  5. Xuequn Shang
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Corresponding authors

Correspondence to Yupei Zhang or Xuequn Shang .

Editor information

Editors and Affiliations

  1. Tongji University, Shanghai, China

    De-Shuang Huang

  2. University of Ulsan, Ulsan, Korea (Republic of)

    Kang-Hyun Jo

  3. Xi’an Polytechnic University, Xi’an, China

    Junfeng Jing

  4. The University of Wollongong, North Wollongong, NSW, Australia

    Prashan Premaratne

  5. Polytecnic of Bari, Bari, Italy

    Vitoantonio Bevilacqua

  6. Liverpool John Moores University, Liverpool, UK

    Abir Hussain

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Zhang, Y., Khan, M.S.I., Zhou, Y., Xiao, M., Shang, X. (2022). An Effective Chinese Text Classification Method with Contextualized Weak Supervision for Review Autograding. In: Huang, DS., Jo, KH., Jing, J., Premaratne, P., Bevilacqua, V., Hussain, A. (eds) Intelligent Computing Methodologies. ICIC 2022. Lecture Notes in Computer Science(), vol 13395. Springer, Cham. https://doi.org/10.1007/978-3-031-13832-4_15

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  • DOI: https://doi.org/10.1007/978-3-031-13832-4_15

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-13831-7

  • Online ISBN: 978-3-031-13832-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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