References
- S. Minaee, N. Kalchbrenner, E. Cambria, N. Nikzad, M. Chenaghlu, J. Gao, “Deep learning--based text classification: a comprehensive review,” ACM Computing Surveys (CSUR), vol. 54(3), April. 2022, pp. 1–40, doi:10.1145/3439726.
- H. Cai, VW Zheng, KCC Chang, “A comprehensive survey of graph embedding: Problems, techniques, and applications,” IEEE Transactions on Knowledge and Data Engineering, vol. 30(9), Sept. 2018, pp 1616–1637. doi:10.1109/TKDE.2018.2807452.
- PW Battaglia, JB Hamrick, V. Bapst, et al. “Relational inductive biases, deep learning, and graph networks,” arXiv preprint, 2018. doi:10.48550/arXiv.1806.01261.
- D. Shen, G. Wang, W. Wang, et al. “Baseline needs more love: On simple word-embedding-based models and associated pooling mechanisms,” arXiv preprint, 2018, doi:10.48550/arXiv.1805.09843.
- Chen Y, “Convolutional neural network for sentence classification,” University of Waterloo, August 2015, http://hdl.handle.net/10012/9592.
- S. Lai, L. Xu, K Liu, J. Zhao, “Recurrent convolutional neural networks for text classification,” Twenty-ninth AAAI conference on artificial intelligence, 2015.
- R. Mihalcea, P. Tarau, “Textrank: Bringing order into text”, Proceedings of the 2004 conference on empirical methods in natural language processing, 2004, pp 404–411.
- L. Yao, C. Mao, Y. Luo, “Graph convolutional networks for text classification,” Proceedings of the AAAI conference on artificial intelligence, vol. 33(01), July 2019, pp 7370–7377, doi:10.1609/aaai.v33i01.33017370.
- Y. Zhang, X. Yu, Z. Cui, et al. “Every document owns its structure: Inductive text classification via graph neural networks,” arXiv preprint, April 2020, doi:10.48550/arXiv.2004.13826.
- X. Liu, X. You, X. Zhang, J. Wu, P. Lv, “Tensor graph convolutional networks for text classification,” Proceedings of the AAAI conference on artificial intelligence, vol. 34(05), April 2020, pp 8409–8416, doi:10.1609/aaai.v34i05.6359.
- TN Kipf, M. Welling, “Semi-supervised classification with graph convolutional networks,” arXiv preprint, September 2016, doi:10.48550/arXiv.1609.02907.
- X. Zhang, J. Zhao, Y. LeCun, “Character-level convolutional networks for text classification,” Advances in neural information processing systems, 2015.
- DM Blei, AY Ng, MI Jordan, “Latent dirichlet allocation,”. Journal of machine Learning research, January 2003, pp 993–1022.
- R. Wang, Z. Li, J. Cao, T. Chen, L. Wang, “Convolutional recurrent neural networks for text classification,” 2019 International Joint Conference on Neural Networks (IJCNN), IEEE, September 2019, pp 1–6, doi:10.1109/IJCNN.2019.8852406.
- Y. KIM, “Convolutional neural networks for sentence classification,” 2014 Conference on Empirical Methods in Natural Language Processing, 2014, pp 1746–1751.
- Q. Li, H. Peng, J. Li, et al. “A survey on text classification: From shallow to deep learning,” arXiv preprint, August 2020, doi:10.48550/arXiv.2008.00364.
- K. Kowsari, K. Jafari Meimandi, M. Heidarysafa, S. Mendu, L. Barnes, D. Brown, “Text classification algorithms: A survey,”. Information, vol 10(4), October 2019, pp 150, doi:10.3390/info10040150.
- M. Malekzadeh, P. Hajibabaee, M. Heidari, S. Zad, O. Uzuner, J. H Jones, “Review of graph neural network in text classification,” 2021 IEEE 12th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON), IEEE, December 2021, pp 0084–0091, doi:10.1109/UEMCON53757.2021.9666633.
- S. Ghosh, S. Maji, MS. Desarkar, “Graph Neural Network Enhanced Language Models for Efficient Multilingual Text Classification,” arXiv preprint, March 2022, doi:10.48550/arXiv.2203.02912.