GAN-CITE: leveraging semi-supervised generative adversarial networks for citation function classification with limited data
Issued Date
2025-01-01
Resource Type
ISSN
01389130
eISSN
15882861
Scopus ID
2-s2.0-85217178808
Journal Title
Scientometrics
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SCOPUS
Bibliographic Citation
Scientometrics (2025)
Suggested Citation
Chatrinan K., Noraset T., Tuarob S. GAN-CITE: leveraging semi-supervised generative adversarial networks for citation function classification with limited data. Scientometrics (2025). doi:10.1007/s11192-025-05233-1 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/105290
Title
GAN-CITE: leveraging semi-supervised generative adversarial networks for citation function classification with limited data
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Author's Affiliation
Corresponding Author(s)
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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.