Research on Topic Fusion Graph Convolution Network News Text Classification Algorithm

[1] 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. MinaeeS. KalchbrennerN. CambriaE. NikzadN. ChenaghluM. GaoJ. “Deep learning--based text classification: a comprehensive review,” ACM Computing Surveys (CSUR) 54 3 April 2022 1 40 10.1145/3439726 Open DOISearch in Google Scholar

[2] 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. CaiH. ZhengVW ChangKCC “A comprehensive survey of graph embedding: Problems, techniques, and applications,” IEEE Transactions on Knowledge and Data Engineering 30 9 Sept. 2018 1616 1637 10.1109/TKDE.2018.2807452 Open DOISearch in Google Scholar

[3] 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. BattagliaPW HamrickJB BapstV. “Relational inductive biases, deep learning, and graph networks,” arXiv preprint 2018 10.48550/arXiv.1806.01261 Open DOISearch in Google Scholar

[4] 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. ShenD. WangG. WangW. “Baseline needs more love: On simple word-embedding-based models and associated pooling mechanisms,” arXiv preprint 2018 10.48550/arXiv.1805.09843 Open DOISearch in Google Scholar

[5] Chen Y, “Convolutional neural network for sentence classification,” University of Waterloo, August 2015, http://hdl.handle.net/10012/9592. ChenY “Convolutional neural network for sentence classification,” University of Waterloo August 2015 http://hdl.handle.net/10012/9592. Search in Google Scholar

[6] S. Lai, L. Xu, K Liu, J. Zhao, “Recurrent convolutional neural networks for text classification,” Twenty-ninth AAAI conference on artificial intelligence, 2015. LaiS. XuL. LiuK ZhaoJ. “Recurrent convolutional neural networks for text classification,” Twenty-ninth AAAI conference on artificial intelligence 2015 Search in Google Scholar

[7] 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. MihalceaR. TarauP. “Textrank: Bringing order into text” Proceedings of the 2004 conference on empirical methods in natural language processing 2004 404 411 Search in Google Scholar

[8] 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. YaoL. MaoC. LuoY. “Graph convolutional networks for text classification,” Proceedings of the AAAI conference on artificial intelligence 33 01 July 2019 7370 7377 10.1609/aaai.v33i01.33017370 Open DOISearch in Google Scholar

[9] 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. ZhangY. YuX. CuiZ. “Every document owns its structure: Inductive text classification via graph neural networks,” arXiv preprint April 2020 10.48550/arXiv.2004.13826 Open DOISearch in Google Scholar

[10] 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. LiuX. YouX. ZhangX. WuJ. LvP. “Tensor graph convolutional networks for text classification,” Proceedings of the AAAI conference on artificial intelligence 34 05 April 2020 8409 8416 10.1609/aaai.v34i05.6359 Open DOISearch in Google Scholar

[11] TN Kipf, M. Welling, “Semi-supervised classification with graph convolutional networks,” arXiv preprint, September 2016, doi:10.48550/arXiv.1609.02907. KipfTN WellingM. “Semi-supervised classification with graph convolutional networks,” arXiv preprint September 2016 10.48550/arXiv.1609.02907 Open DOISearch in Google Scholar

[12] X. Zhang, J. Zhao, Y. LeCun, “Character-level convolutional networks for text classification,” Advances in neural information processing systems, 2015. ZhangX. ZhaoJ. LeCunY. “Character-level convolutional networks for text classification,” Advances in neural information processing systems 2015 Search in Google Scholar

[13] DM Blei, AY Ng, MI Jordan, “Latent dirichlet allocation,”. Journal of machine Learning research, January 2003, pp 993–1022. BleiDM NgAY JordanMI “Latent dirichlet allocation,” Journal of machine Learning research January 2003 993 1022 Search in Google Scholar

[14] 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. WangR. LiZ. CaoJ. ChenT. WangL. “Convolutional recurrent neural networks for text classification,” 2019 International Joint Conference on Neural Networks (IJCNN) IEEE September 2019 1 6 10.1109/IJCNN.2019.8852406 Open DOISearch in Google Scholar

[15] Y. KIM, “Convolutional neural networks for sentence classification,” 2014 Conference on Empirical Methods in Natural Language Processing, 2014, pp 1746–1751. KIMY. “Convolutional neural networks for sentence classification,” 2014 Conference on Empirical Methods in Natural Language Processing 2014 1746 1751 Search in Google Scholar

[16] 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. LiQ. PengH. LiJ. “A survey on text classification: From shallow to deep learning,” arXiv preprint August 2020 10.48550/arXiv.2008.00364 Open DOISearch in Google Scholar

[17] 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. KowsariK. Jafari MeimandiK. HeidarysafaM. MenduS. BarnesL. BrownD. “Text classification algorithms: A survey,” Information 10 4 October 2019 150 10.3390/info10040150 Open DOISearch in Google Scholar

[18] 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. MalekzadehM. HajibabaeeP. HeidariM. ZadS. UzunerO. JonesJ. H “Review of graph neural network in text classification,” 2021 IEEE 12th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON) IEEE December 2021 0084 0091 10.1109/UEMCON53757.2021.9666633 Open DOISearch in Google Scholar

[19] 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. GhoshS. MajiS. DesarkarMS. “Graph Neural Network Enhanced Language Models for Efficient Multilingual Text Classification,” arXiv preprint March 2022 10.48550/arXiv.2203.02912 Open DOISearch in Google Scholar