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BertGCN: Transductive Text Classification by Combining GNN and BERT
Open accessAbstract
In this work, we propose BertGCN, a model that combines large scale pretraining and transductive learning for text classification.Bert-GCN constructs a heterogeneous graph over the dataset and represents documents as nodes using BERT representations.By jointly training the BERT and GCN modules within Bert-GCN, the proposed model is able to leverage the advantages of both worlds: large-scale pretraining which takes the advantage of the massive amount of raw data and transductive learning which jointly learns representations for both training data and unlabeled test data by propagating label influence through graph convolution.Experiments show that BertGCN achieves SOTA performances on a wide range of text classification datasets.1
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“It also captures semantic relations previously not emphasized by existing sequential models [28], [29].”
From Emotion Classification With Visibility Graphs (2025) · cites this paper as [28]
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Metadata
- Authors
- Lin, Y., Meng, Y., Sun, X., Han, Q., Kuang, K., Li, J., & Wu, F.
- Affiliations
- Zhejiang University (CN)
- Venue
- Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021
- Published
- 2021
- Pages
- 1456-1462
- OpenAlex
- W3173753074
- Cited by
- 228
- References
- 52
- Citations by year
- 2026: 20 · 2025: 60 · 2024: 63 · 2023: 50 · 2022: 32 · 2021: 3
- FWCI
- 17.09
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Findings of the Association for Computational Linguistics: ACL (Findings of ACL) (publishedVersion, cc-by)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Advanced Graph Neural Networks
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Text and Document Classification Technologies
- Keywords
- text classification, graph convolutional networks, BERT, transductive learning, heterogeneous graphs, document representation, label propagation, pre-training, unlabeled test data
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