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

Krittin Chatrinan, Thanapon Noraset and Suppawong Tuarob ()
Additional contact information
Krittin Chatrinan: Mahidol University
Thanapon Noraset: Mahidol University
Suppawong Tuarob: Mahidol University

Scientometrics, 2025, vol. 130, issue 2, No 7, 679-703

Abstract: 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.

Keywords: Citation function classification; Semi-supervised learning; Generative adversarial networks; Scholarly big data (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s11192-025-05233-1 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:scient:v:130:y:2025:i:2:d:10.1007_s11192-025-05233-1

Ordering information: This journal article can be ordered from
http://www.springer.com/economics/journal/11192

DOI: 10.1007/s11192-025-05233-1

Access Statistics for this article

Scientometrics is currently edited by Wolfgang Glänzel

More articles in Scientometrics from Springer, Akadémiai Kiadó
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().