TempCL-Cite: Leveraging Temporal Signals and LLM Positives in Contrastive Learning for Citation Intent Classification

Citation intent classification identifies the underlying functions of scholarly citations and provides semantic information beyond citation counts. However, existing methods pay limited attention to temporal information in citation relationships and are often constrained by the scarcity of high-quality annotated data. To address these challenges, we propose TempCL-Cite, a temporal-aware contrastive learning framework that jointly exploits publication time gaps and large language model (LLM)-generated positive examples. TempCL-Cite encodes the publication time gap and fuses it with textual representations, while using temporal-gap differences together with label differences to guide negative sampling. In addition, a prompt-based LLM generates paraphrases of citation contexts conditioned on their intent labels, providing additional positive views for contrastive learning. The LLM is used only for offline data augmentation and is not required during inference. Experiments on three benchmark datasets show that TempCL-Cite achieves the highest mean Accuracy and Macro-F1 among the compared methods. Ablation results indicate that temporal modeling and contrastive learning contribute to performance in the evaluated settings. Furthermore, temporal-gap-constrained negative sampling outperforms both label-only selection and matched-number random sampling on all three datasets. Comparisons with same-label positives and dropout views show that the proposed LLM-based positive construction strategy achieves the highest mean scores among the evaluated alternatives. BERTScore results indicate strong semantic similarity between the generated and original citation contexts.