{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,8]],"date-time":"2026-01-08T07:05:35Z","timestamp":1767855935732,"version":"3.49.0"},"reference-count":21,"publisher":"Institute of Electronics, Information and Communications Engineers (IEICE)","issue":"4","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEICE Trans. Inf. & Syst."],"published-print":{"date-parts":[[2024,4,1]]},"DOI":"10.1587\/transinf.2023ihp0013","type":"journal-article","created":{"date-parts":[[2024,3,31]],"date-time":"2024-03-31T22:20:29Z","timestamp":1711923629000},"page":"451-458","source":"Crossref","is-referenced-by-count":2,"title":["Pattern-Based Meta Graph Neural Networks for Argument Classifications"],"prefix":"10.1587","volume":"E107.D","author":[{"given":"Shiyao","family":"DING","sequence":"first","affiliation":[{"name":"Graduate School of Informatics, Kyoto University"}]},{"given":"Takayuki","family":"ITO","sequence":"additional","affiliation":[{"name":"Graduate School of Informatics, Kyoto University"}]}],"member":"532","reference":[{"key":"1","doi-asserted-by":"crossref","unstructured":"[1] X. Hua and L. Wang, \u201cEfficient argument structure extraction with transfer learning and active learning,\u201d Findings of the Association for Computational Linguistics: ACL 2022, Dublin, Ireland, pp.423-437, Association for Computational Linguistics, May 2022. 10.18653\/v1\/2022.findings-acl.36","DOI":"10.18653\/v1\/2022.findings-acl.36"},{"key":"2","doi-asserted-by":"publisher","unstructured":"[2] A. Peldszus and M. Stede, \u201cFrom argument diagrams to argumentation mining in texts: A survey,\u201d International Journal of Cognitive Informatics and Natural Intelligence (IJCINI), vol.7, no.1, pp.1-31, 2013. 10.4018\/jcini.2013010101","DOI":"10.4018\/jcini.2013010101"},{"key":"3","doi-asserted-by":"crossref","unstructured":"[3] E. Cabrio and S. Villata, \u201cFive years of argument mining: a data-driven analysis.,\u201d IJCAI, pp.5427-5433, 2018. 10.24963\/ijcai.2018\/766","DOI":"10.24963\/ijcai.2018\/766"},{"key":"4","doi-asserted-by":"publisher","unstructured":"[4] M. Lippi and P. Torroni, \u201cArgumentation mining: State of the art and emerging trends,\u201d ACM Transactions on Internet Technology (TOIT), vol.16, no.2, pp.1-25, 2016. 10.1145\/2850417","DOI":"10.1145\/2850417"},{"key":"5","unstructured":"[5] P.W. Battaglia, J.B. Hamrick, V. Bapst, A. Sanchez-Gonzalez, V. Zambaldi, M. Malinowski, A. Tacchetti, D. Raposo, A. Santoro, R. Faulkner, et al., \u201cRelational inductive biases, deep learning, and graph networks,\u201d arXiv preprint arXiv:1806.01261, 2018."},{"key":"6","doi-asserted-by":"crossref","unstructured":"[6] F. Zhou, C. Cao, K. Zhang, G. Trajcevski, T. Zhong, and J. Geng, \u201cMeta-gnn: On few-shot node classification in graph meta-learning,\u201d Proc. 28th ACM International Conference on Information and Knowledge Management, pp.2357-2360, 2019. 10.1145\/3357384.3358106","DOI":"10.1145\/3357384.3358106"},{"key":"7","unstructured":"[7] K. Huang and M. Zitnik, \u201cGraph meta learning via local subgraphs,\u201d Advances in Neural Information Processing Systems, vol.33, pp.5862-5874, 2020."},{"key":"8","doi-asserted-by":"crossref","unstructured":"[8] A. Sankar, X. Zhang, and K.C.-C. Chang, \u201cMeta-gnn: Metagraph neural network for semi-supervised learning in attributed heterogeneous information networks,\u201d Proc. 2019 IEEE\/ACM International Conference on Advances in Social Networks Analysis and Mining, pp.137-144, 2019. 10.1145\/3341161.3342859","DOI":"10.1145\/3341161.3342859"},{"key":"9","doi-asserted-by":"publisher","unstructured":"[9] T. Ito, S. Suzuki, N. Yamaguchi, T. Nishida, K. Hiraishi, and K. Yoshino, \u201cD-agree: crowd discussion support system based on automated facilitation agent,\u201d Proc. AAAI conference on artificial intelligence, vol.34, no.09, pp.13614-13615, 2020. 10.1609\/aaai.v34i09.7094","DOI":"10.1609\/aaai.v34i09.7094"},{"key":"10","doi-asserted-by":"crossref","unstructured":"[10] M. Schlichtkrull, T.N. Kipf, P. Bloem, R.v.d. Berg, I. Titov, and M. Welling, \u201cModeling relational data with graph convolutional networks,\u201d European semantic web conference, pp.593-607, Springer, 2018. 10.1007\/978-3-319-93417-4_38","DOI":"10.1007\/978-3-319-93417-4_38"},{"key":"11","doi-asserted-by":"publisher","unstructured":"[11] C. Stab and I. Gurevych, \u201cParsing argumentation structures in persuasive essays,\u201d Computational Linguistics, vol.43, no.3, pp.619-659, 2017. 10.1162\/coli_a_00295","DOI":"10.1162\/COLI_a_00295"},{"key":"12","unstructured":"[12] S. Suzuki, T. Ito, A. Moustafa, and R. Hadfi, \u201cA node classification approach for dynamically extracting the structures of online discussions,\u201d The 34th Annual Conference of the Japanese Society for Artificial Intelligence, vol.2G5-ES-3, no.02, pp.1-4, 2020."},{"key":"13","unstructured":"[13] W. Kunz and H.W. Rittel, Issues as elements of information systems, Citeseer, 1970."},{"key":"14","doi-asserted-by":"crossref","unstructured":"[14] I. Kuhlmann and M. Thimm, \u201cUsing graph convolutional networks for approximate reasoning with abstract argumentation frameworks: A feasibility study,\u201d International Conference on Scalable Uncertainty Management, pp.24-37, Springer, 2019.","DOI":"10.1007\/978-3-030-35514-2_3"},{"key":"15","doi-asserted-by":"crossref","unstructured":"[15] D. Craandijk and F. Bex, \u201cDeep learning for abstract argumentation semantics,\u201d arXiv preprint arXiv:2007.07629, 2020.","DOI":"10.24963\/ijcai.2020\/231"},{"key":"16","unstructured":"[16] F. Ruggeri, M. Lippi, and P. Torroni, \u201cTree-constrained graph neural networks for argument mining,\u201d arXiv preprint arXiv:2110.00124, 2021."},{"key":"17","doi-asserted-by":"publisher","unstructured":"[17] Y. Chang, C. Chen, W. Hu, Z. Zheng, X. Zhou, and S. Chen, \u201cMegnn: Meta-path extracted graph neural network for heterogeneous graph representation learning,\u201d Knowledge-Based Systems, vol.235, p.107611, 2022. 10.1016\/j.knosys.2021.107611","DOI":"10.1016\/j.knosys.2021.107611"},{"key":"18","doi-asserted-by":"crossref","unstructured":"[18] Z. Luo, J. Lian, H. Huang, H. Jin, and X. Xie, \u201cAda-gnn: Adapting to local patterns for improving graph neural networks,\u201d Proc. Fifteenth ACM International Conference on Web Search and Data Mining, pp.638-647, 2022. 10.1145\/3488560.3498460","DOI":"10.1145\/3488560.3498460"},{"key":"19","doi-asserted-by":"crossref","unstructured":"[19] P.M. Dung, \u201cOn the acceptability of arguments and its fundamental role in nonmonotonic reasoning, logic programming and n-person games,\u201d Artificial intelligence, vol.77, no.2, pp.321-357, 1995. 10.1016\/0004-3702(94)00041-x","DOI":"10.1016\/0004-3702(94)00041-X"},{"key":"20","unstructured":"[20] Y. Dong, S. Ding, J. Haqbeen, and T. Ito, \u201cThe significant factors that affect the accuracy on classifying english ibis datasets,\u201d The 85th National Convention of IPSJ, March 2023."},{"key":"21","unstructured":"[21] T.N. Kipf and M. Welling, \u201cSemi-supervised classification with graph convolutional networks,\u201d arXiv preprint arXiv:1609.02907, 2016."}],"container-title":["IEICE Transactions on Information and Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transinf\/E107.D\/4\/E107.D_2023IHP0013\/_pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,4,6]],"date-time":"2024-04-06T04:27:21Z","timestamp":1712377641000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transinf\/E107.D\/4\/E107.D_2023IHP0013\/_article"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,4,1]]},"references-count":21,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2024]]}},"URL":"https:\/\/doi.org\/10.1587\/transinf.2023ihp0013","relation":{},"ISSN":["0916-8532","1745-1361"],"issn-type":[{"value":"0916-8532","type":"print"},{"value":"1745-1361","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,4,1]]},"article-number":"2023IHP0013"}}