Heterogeneous hypergraph learning for literature retrieval based on citation intents
Author:
Funder
National Natural Science Foundation of China
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s11192-024-05066-4.pdf
Reference51 articles.
1. Antelmi, A., Cordasco, G., Polato, M., Scarano, V., Spagnuolo, C., & Yang, D. (2023). A survey on hypergraph representation learning. ACM Computering Survey, 56(1). https://doi.org/10.1145/3605776
2. Bai, S., Zhang, F., & Torr, P.H. (2021). Hypergraph convolution and hypergraph attention. Pattern Recognition, 110, 107637. https://doi.org/10.1016/j.patcog.2020.107637
3. Beltagy, I., Lo, K., & Cohan, A. (2019). SciBERT: A pretrained language model for scientific text. arXiv preprint arXiv:1903.10676,
4. Bhagavatula, C., Feldman, S., Power, R., & Ammar, W. (2018). Content-based citation recommendation. arXiv preprint arXiv:1802.08301,
5. Caciularu, A., Cohan, A., Beltagy, I., Peters, M., Cattan, A., Dagan, I. (2021). CDLM: Cross-document language modeling. In M.-F. Moens, X. Huang, L. Specia, & S. W.-t. Yih (Eds.), Findings of the association for computational linguistics: Emnlp 2021 (pp. 2648–2662). Dominican RepublicAssociation for Computational Linguistics.https://aclanthology.org/2021.findings-emnlp.225
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