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GAN-CITE: leveraging semi-supervised generative adversarial networks for citation function classification with limited data

Author

Listed:
  • Krittin Chatrinan

    (Mahidol University)

  • Thanapon Noraset

    (Mahidol University)

  • Suppawong Tuarob

    (Mahidol University)

Abstract
Citation function analysis is crucial to understanding how cited literature contributes to the overall discourse and meaning conveyed in scientific publications. Citation functions serve diverse roles that must be accurately identified and categorized. Still, the field of citation function analysis faces challenges due to limited labeled data and the complexity of defining and categorizing citation functions, which require expertise and a deep understanding of scientific literature. This limitation results in imprecise identification and categorization of citation functions, emphasizing the need for further advancements to improve the accuracy and reliability of citation function analysis. This paper proposes GAN-CITE, a novel framework employing semi-supervised learning techniques to address these limitations. Its primary objective is to efficiently leverage available unlabeled data by combining generative adversarial networks (GANs) and the language model to incorporate substantial data representations from unlabeled data sources. Our study demonstrates that GAN-CITE outperforms both supervised and semi-supervised state-of-the-art models in limited data settings, namely 10%, 20%, and 30% of the total labeled data. We also examine its performance in insufficient and imbalanced labeled data situations, as well as the potential of unlabeled data utilization. These findings highlight the success of generative adversarial networks in enhancing citation function classification and their applications in digital libraries that require precise citation function categorization, such as trend analysis and impact quantification, under limited annotated data.

Suggested Citation

  • Krittin Chatrinan & Thanapon Noraset & Suppawong Tuarob, 2025. "GAN-CITE: leveraging semi-supervised generative adversarial networks for citation function classification with limited data," Scientometrics, Springer;Akadémiai Kiadó, vol. 130(2), pages 679-703, February.
    • Handle: RePEc:spr:scient:v:130:y:2025:i:2:d:10.1007_s11192-025-05233-1
    DOI: 10.1007/s11192-025-05233-1
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    References listed on IDEAS

    as
    1. Xin An & Xin Sun & Shuo Xu, 2022. "Important citations identification with semi-supervised classification model," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(11), pages 6533-6555, November.
    2. Ruihua Qi & Jia Wei & Zhen Shao & Zhengguang Li & Heng Chen & Yunhao Sun & Shaohua Li, 2023. "Multi-task learning model for citation intent classification in scientific publications," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(12), pages 6335-6355, December.
    3. Setio Basuki & Masatoshi Tsuchiya, 2022. "SDCF: semi-automatically structured dataset of citation functions," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(8), pages 4569-4608, August.
    4. Chanathip Pornprasit & Xin Liu & Pattararat Kiattipadungkul & Natthawut Kertkeidkachorn & Kyoung-Sook Kim & Thanapon Noraset & Saeed-Ul Hassan & Suppawong Tuarob, 2022. "Enhancing citation recommendation using citation network embedding," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(1), pages 233-264, January.
    5. Ma, Shutian & Zhang, Chengzhi & Zhang, Heng & Gao, Zheng, 2025. "Citation recommendation based on argumentative zoning of user queries," Journal of Informetrics, Elsevier, vol. 19(1).
    6. Milena Vuletić & Felix Prenzel & Mihai Cucuringu, 2024. "Fin-GAN: forecasting and classifying financial time series via generative adversarial networks," Quantitative Finance, Taylor & Francis Journals, vol. 24(2), pages 175-199, January.
    7. Yang Zhang & Rongying Zhao & Yufei Wang & Haihua Chen & Adnan Mahmood & Munazza Zaib & Wei Emma Zhang & Quan Z. Sheng, 2022. "Correction to: Towards employing native information in citation function classification," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(11), pages 6579-6579, November.
    8. Yang Zhang & Rongying Zhao & Yufei Wang & Haihua Chen & Adnan Mahmood & Munazza Zaib & Wei Emma Zhang & Quan Z. Sheng, 2022. "Towards employing native information in citation function classification," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(11), pages 6557-6577, November.
    9. Xiaorui Jiang & Jingqiang Chen, 2023. "Contextualised segment-wise citation function classification," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(9), pages 5117-5158, September.
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