Li, C., Peng, X. orcid.org/0000-0001-5787-9982, Peng, H. et al. (2 more authors) (2021) TextGTL : graph-based transductive learning for semi-supervised text classification via structure-sensitive interpolation. In: Zhou, Z.-H., (ed.) Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence. IJCAI - 30th International Joint Conference on Artificial Intelligence, 19-27 Aug 2021, Montreal, Canada. International Joint Conferences on Artificial Intelligence Organization (IJCAI) , pp. 2680-2686. ISBN 9780999241196
Abstract
Compared with traditional sequential learning models, graph-based neural networks exhibit excellent properties when encoding text, such as the capacity of capturing global and local information simultaneously. Especially in the semi-supervised scenario, propagating information along the edge can effectively alleviate the sparsity of labeled data. In this paper, beyond the existing architecture of heterogeneous word-document graphs, for the first time, we investigate how to construct lightweight non-heterogeneous graphs based on different linguistic information to better serve free text representation learning. Then, a novel semi-supervised framework for text classification that refines graph topology under theoretical guidance and shares information across different text graphs, namely Text-oriented Graph-based Transductive Learning (TextGTL), is proposed. TextGTL also performs attribute space interpolation based on dense substructure in graphs to predict low-entropy labels with high-quality feature nodes for data augmentation. To verify the effectiveness of TextGTL, we conduct extensive experiments on various benchmark datasets, observing significant performance gains over conventional heterogeneous graphs. In addition, we also design ablation studies to dive deep into the validity of components in TextTGL.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Editors: |
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Copyright, Publisher and Additional Information: | © 2021 International Joint Conferences on Artificial Intelligence. |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 08 Jul 2022 08:54 |
Last Modified: | 08 Jul 2022 08:54 |
Status: | Published |
Publisher: | International Joint Conferences on Artificial Intelligence Organization (IJCAI) |
Refereed: | Yes |
Identification Number: | 10.24963/ijcai.2021/369 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:188378 |