Gan-Cite

Leveraging semi-supervised generative adversarial networks for citation function classification with limited data

Bibliographic Data

ID21441893
AuthorsKrittin Chatrinan (Mahidol University), Thanapon Noraset (0000-0002-7080-6523, Mahidol University), Suppawong Tuarob (0000-0002-5201-5699, Mahidol University, corresponding author)
Year2025
Volume130
Issue2
Pages679-703
Publication date2025-02-01
Open AccessYes
TypeARTICLE
VenueScientometrics (JOURNAL)
Journal identifiersISSN: 0138-9130 • E-ISSN: 1588-2861
PublisherSpringer Science and Business Media LLC (PUBLISHER)
DOI10.1007/s11192-025-05233-1
OpenAlexW4406918849
LanguageEN
Citations received1
References cited31

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

Adversarial system · Citation · Function (biology) · Generative grammar · Machine learning · World Wide Web · Artificial Intelligence · Biomedical Text Mining and Ontologies · Computer Science · Machine Learning in Healthcare · Topic Modeling

Tools

Open DOIOpen Access