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Citation function and citation sentiment are two essential aspects ofcitation content analysis (CCA), which are useful for influence analysis, therecommendation of scientific publications. However, existing studies are mostlytraditional machine learning methods, although deep learning techniques havealso been explored, the improvement of the performance seems not significantdue to insufficient training data, which brings difficulties to applications.In this paper, we propose to fine-tune pre-trained contextual embeddingsULMFiT, BERT, and XLNet for the task. Experiments on three public datasets showthat our strategy outperforms all the baselines in terms of the F1 score. Forcitation function identification, the XLNet model achieves 87.2%, 86.90%, and81.6% on DFKI, UMICH, and TKDE2019 datasets respectively, while it achieves91.72% and 91.56% on DFKI and UMICH in term of citation sentimentidentification. Our method can be used to enhance the influence analysis ofscholars and scholarly publications.

Fine-tuning Pre-trained Contextual Embeddings for Citation Content Analysis in Scholarly Publication