[1] A. Rejeb, K. Rejeb, A. Abdollahi, and H. Treiblmaier, “The big picture on Instagram research: Insights from a bibliometric analysis,” Telemat. Informatics, vol. 73, no. December 2021, p. 101876, 2022, https://doi.org/10.1016/j.tele.2022.101876.
[2] C. Wartena, R. Brussee, and W. Slakhorst, “Keyword extraction using word co-occurrence,” Proc. - 21st Int. Work. Database Expert Syst. Appl. DEXA 2010, IEEE, 2010, pp. 54–58, https://doi.org/10.1109/DEXA.2010.32.
[3] K. S. Hasan and V. Ng, “Automatic keyphrase extraction: A survey of the state of the art,” 52nd Annu. Meet. Assoc. Comput. Linguist. ACL 2014 - Proc. Conf., vol. 1, pp. 1262–1273, 2014, https://doi.org/10.3115/v1/p14-1119.
[4] F. Liu, D. Pennell, F. Liu, and Y. Liu, “Unsupervised approaches for automatic keyword extraction using meeting transcripts,” NAACL HLT 2009 - Hum. Lang. Technol. 2009 Annu. Conf. North Am. Chapter Assoc. Comput. Linguist. Proc. Conf., IEEE, January 2009, pp. 620–628, https://doi.org/10.3115/1620754.1620845.
[5] S. Mirasdar and M. Bedekar, “Graph of Words Model for Natural Language Processing,” in Graph Learning and Network Science for Natural Language Processing, CRC Press, 2023, pp. 1–20.
[6] M. Garg, A survey on different dimensions for graphical keyword extraction techniques: Issues and Challenges, vol. 54, no. 6, pp. 4731–4770, 2021. https://doi.org/10.1007/s10462-021-10010-6
[7] M. I. Fudolig, T. Alshaabi, M. V. Arnold, C. M. Danforth, and P. S. Dodds, “Sentiment and structure in word co-occurrence networks on Twitter,” Appl. Netw. Sci., vol. 7, no. 1, pp. 1–18, 2022, https://doi.org/10.1007/s41109-022-00446-2.
[8] R. Futrell, “Estimating word co-occurrence probabilities from pretrained static embeddings using a log-bilinear model,” C. 2022 - Work. Cogn. Model. Comput. Linguist. Proc. Work., pp. 54–60, 2022, https://doi.org/10.18653/v1/2022.cmcl-1.6.
[9] J. Gabín, M. E. Ares, and J. Parapar, “Keyword Embeddings for Query Suggestion,” In European Conference on Information Retrieval, Cham: Springer Nature Switzerland, 2023, pp. 346–360, https://doi.org/10.1007/978-3-031-28244-7_22.
[10] D. A. Vega-Oliveros, P. S. Gomes, E. E. Milios, and L. Berton, “A multi-centrality index for graph-based keyword extraction,” Inf. Process. Manag., vol. 56, no. 6, p. 102063, 2019, https://doi.org/10.1016/j.ipm.2019.102063.
[11] S. K. Biswas, M. Bordoloi, and J. Shreya, “A graph based keyword extraction model using collective node weight,” Expert Syst. Appl., vol. 97, pp. 51–59, 2018, https://doi.org/10.1016/j.eswa.2017.12.025.
[12] W. Li, J. Xue, X. Zhang, H. Chen, and Z. Chen, “Word-Graph2vec: An efficient word embedding approach on word co-occurrence graph using random walk sampling,” arXiv preprint arXiv:2301.04312, pp. 1–16. https://doi.org/10.48550/arXiv.2301.04312.
[13] H. Hettiarachchi, M. Adedoyin-olowe, J. Bhogal, and M. M. Gaber, “WhatsUp : An event resolution approach for co-occurring events in social media,” Inf. Sci. (Ny)., vol. 625, pp. 553–577, 2023, https://doi.org/10.1016/j.ins.2023.01.001.
[14] R. Nazar and D. Lindemann, “Terminology extraction using co-occurrence patterns as predictors of semantic relevance,” In Proceedings of the Workshop on Terminology in the 21st century: many faces, many places, 2022, pp. 26–29.
[15] F. Valentini, D. F. Slezak, and E. Altszyler, “The Undesirable Dependence on Frequency of Gender Bias Metrics Based on Word Embeddings,” arXiv preprint arXiv:2301.00792, 2023. https://doi.org/10.48550/arXiv.2301.00792.
[16] R. Mihalcea and P. Tarau, “TextRank: Bringing order into texts,” Proc. 2004 Conf. Empir. Methods Nat. Lang. Process. EMNLP 2004 - A Meet. SIGDAT, a Spec. Interes. Gr. ACL held conjunction with ACL 2004, vol. 85, 2004, pp. 404–411.
[17] A. Xiong, D. Liu, H. Tian, Z. Liu, P. Yu, and M. Kadoch, “News keyword extraction algorithm based on semantic clustering and word graph model,” Tsinghua Sci. Technol., vol. 26, no. 6, pp. 886–893, 2021, https://doi.org/10.26599/TST.2020.9010051.
[18] J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, “Bert: Pre-training of deep bidirectional transformers for language understanding,” arXiv Prepr. arXiv1810.04805, 2018. https://doi.org/10.48550/arXiv.1810.04805
[19] L. Chi and L. Hu, “ISKE: An unsupervised automatic keyphrase extraction approach using the iterated sentences based on graph method,” Knowledge-Based Syst., vol. 223, p. 107014, 2021, https://doi.org/10.1016/j.knosys.2021.107014.
[20] L. Gan, Z. Teng, Y. Zhang, L. Zhu, F. Wu, and Y. Yang, “SemGloVe: Semantic Co-Occurrences for GloVe From BERT,” IEEE/ACM Trans. Audio, Speech, Lang. Process., vol. 30, pp. 2696–2704, 2022, https://doi.org/10.1109/TASLP.2022.3197316.
[21] S. Anjali, N. M. Meera, and M. G. Thushara, “A Graph based Approach for Keyword Extraction from Documents,” in 2019 Second International Conference on Advanced Computational and Communication Paradigms (ICACCP), 2019, pp. 1–4, https://doi.org/10.1109/ICACCP.2019.8882946.
[22] L. Zhao, Z. Miao, C. Wang, and W. Kong, “An Unsupervised Keyword Extraction Method based on Text Semantic Graph,” in 2022 IEEE 6th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC ), 2022, pp. 1431–1436, https://doi.org/10.1109/IAEAC54830.2022.9929800.
[23] D. Brock, A. Khan, T. Doan, A. Lin, Y. Guo, and P. Tarau, “Textstar: a Fast and Lightweight Graph-Based Algorithm for Extractive Summarization and Keyphrase Extraction,” Proc. 20th Annu. Work. Australas. Lang. Technol. Assoc., no. 3, pp. 161–169, 2022, [Online]. Available: https://aclanthology.org/2022.alta-1.22.
[24] Y. Ying, T. Qingping, X. Qinzheng, Z. Ping, and L. Panpan, “A Graph-based Approach of Automatic Keyphrase Extraction,” Procedia Comput. Sci., vol. 107, no. Icict, pp. 248–255, 2017, https://doi.org/10.1016/j.procs.2017.03.087.