@inproceedings{oai:ipsj.ixsq.nii.ac.jp:00229815,
 author = {丁, 世堯 and 伊藤, 孝行},
 book = {第85回全国大会講演論文集},
 issue = {1},
 month = {Feb},
 note = {Issue-based information system (IBIS) is a classic argumentation-based approach to solve wicked problems. One of its important problems is how to classify labels for its nodes such as ideas and issues from argumentation documents, which usually causes a huge burden. In this paper, we propose a graph convolutional networks (GCN)-based method that can automatically classify the node labels by efficiently utilizing the relationship of the nodes. Specifically, we consider two kinds of IBIS structures: strict-IBIS and soft-IBIS which are distinguished by whether the argumentation structures strictly follow IBIS definition. We then perform evaluations on a real English discussion dataset to confirm the effectiveness of our proposed method.},
 pages = {27--28},
 publisher = {情報処理学会},
 title = {Graph convolutional networks for node classification in issue-based information system},
 volume = {2023},
 year = {2023}
}