Span-based semantic syntactic dual enhancement for aspect sentiment triplet extraction
Author:
Funder
Natural Science Foundation of Tianjin Municipality
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s10844-024-00881-w.pdf
Reference38 articles.
1. AlBadani, B., Dong, J., Shi, R., Al-Sabri, R., Saeed, D., & Thobhani, A. (2023). Smgnn: span-to-span multi-channel graph neural network for aspect-sentiment triplet extraction. Journal of Intelligent Information Systems, 61(3), 695–715. https://doi.org/10.1007/s10844-023-00794-0
2. Chamid, A. A., & Widowati, Kusumaningrum R. (2022). Graph-based semi-supervised deep learning for indonesian aspect-based sentiment analysis. Big Data and Cognitive Computing, 7(1), 5. https://doi.org/10.3390/bdcc7010005
3. Chen, H., Zhai, Z., Feng, F., Li, R., & Wang, X. (2022). Enhanced multi-channel graph convolutional network for aspect sentiment triplet extraction. In Proceedings of the 60th annual meeting of the association for computational linguistics (Volume 1: Long Papers) (pp. 2974–2985). https://doi.org/10.18653/v1/2022.acl-long.212
4. Chen, S., Wang, Y., Liu, J., & Wang, Y. (2021) Bidirectional machine reading comprehension for aspect sentiment triplet extraction. In Proceedings of the AAAI conference on artificial intelligence (pp. 12666–12674). https://doi.org/10.1609/aaai.v35i14.17500
5. Dai, D., Chen, T., Xia, S., Wang, G., & Chen, Z. (2022). Double embedding and bidirectional sentiment dependence detector for aspect sentiment triplet extraction. Knowledge-Based Systems, 253, 109506. https://doi.org/10.1016/j.knosys.2022.109506
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