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Leveraging deep learning for automatic literature screening in intelligent bibliometrics

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Abstract

Intelligent bibliometrics, by providing sufficient statistical information based on large-scale literature data analytics, is promising for understanding innovative pathways, addressing meaningful insights with the assistance of expert knowledge, and indicating key areas of scientific inquiry. However, the exponential growth of global scientific publication output in most areas of modern science makes it extremely difficult and labor-intensive to analyze literature in large volumes. This study aims to accelerate intelligent bibliometrics-driven literature analysis by leveraging deep learning for automatic literature screening. The comparison of different machine learning algorithms for the automatic classification of literature regarding relevance to a given research topic reveals the outstanding performance of deep learning. This study also compares different features as model input and provides suggestions about training dataset size. By leveraging deep learning’s abilities in predictive and big data analytics, this study makes contributions to intelligent bibliometrics by promoting literature screening and is promising to track technological changes and scientific evolutionary pathways.

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Data availability

The datasets used in this study are available from the corresponding author upon reasonable request.

Notes

  1. https://fasttext.cc/docs/en/english-vectors.html

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Acknowledgements

The research described in this article has been supported by a grant from the Research Grants Council of the Hong Kong Special Administrative Region, China (UGC/FDS16/E01/19), the Direct Grant (DR22A2) and the Faculty Research Grants (DB22B4 and DB22B7) of Lingnan University, Hong Kong, the One-off Special Fund from Central and Faculty Fund in Support of Research from 2019/20 to 2021/22 (MIT02/19-20), and Interdisciplinary Research Scheme of Dean’s Research Fund 2021/22 (FLASS/DRF/IDS-3) of The Education University of Hong Kong.

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Authors and Affiliations

  1. School of Education, Guangzhou University, Guangzhou, China

    Xieling Chen

  2. Department of Computing and Decision Sciences, Lingnan University, Hong Kong SAR, China

    Haoran Xie

  3. School of Science and Technology, Hong Kong Metropolitan University, Hong Kong SAR, China

    Zongxi Li & Fu Lee Wang

  4. College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China

    Dian Zhang

  5. Department of Mathematics and Information Technology, The Education University of Hong Kong, Hong Kong SAR, China

    Gary Cheng

  6. Department of Computer Science, Hong Kong Baptist University, Hong Kong SAR, China

    Hong-Ning Dai

  7. Department of Computing, The Hong Kong Polytechnic University, Hong Kong SAR, China

    Qing Li

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  1. Xieling Chen
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Appendix

Appendix

See Tables 10, 11, 12, 13, 14, 15, 16, 17

Table 10 Performance of accuracy of different classification algorithms with title feature
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Table 11 Performance of F1 of different classification algorithms with title feature
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Table 12 Performance of accuracy of different classification algorithms with abstract feature
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Table 13 Performance of F1 of different classification algorithms with abstract feature
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Table 14 Performance of accuracy of different classification algorithms with all feature
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Table 15 Performance of F1 of different classification algorithms with all feature
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Table 16 Post Hoc Comparisons using Tukey’s Test with accuracy as dependent variable
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Table 17 Post Hoc Comparisons using Tukey’s Test with F1-score as dependent variable
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and Figs. 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22 

Fig. 11
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Accuracy and F1-score for classroom dialogue

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Fig. 12
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Accuracy and F1-score for technology-enhanced classroom dialogue

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Accuracy and F1-score for NLP-enhanced clinical trial

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Accuracy and F1-score for game-based collaborative learning

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Fig. 15
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Accuracy and F1-score for technology-enhanced language learning

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Accuracy and F1-score for user modeling

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Accuracy of different algorithms when using title information

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Fig. 18
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F1-score of different algorithms when using title information

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Fig. 19
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Accuracy of different algorithms when using abstract information

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F1-score of different algorithms when using abstract information

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Fig. 21
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Accuracy of different algorithms when using all information

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Fig. 22
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F1-score of different algorithms when using all information

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Chen, X., Xie, H., Li, Z. et al. Leveraging deep learning for automatic literature screening in intelligent bibliometrics. Int. J. Mach. Learn. & Cyber. 14, 1483–1525 (2023). https://doi.org/10.1007/s13042-022-01710-8

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