BertGCN: Transductive Text Classification by Combining GNN and BERT

By Yuxiao Lin, Yuxian Meng, Xiaofei Sun, Qinghong Han, Kun Kuang, Jiwei Li, Fei Wu. Findings, 2021

317 citations recorded by the source corpus.

In this work, we propose BertGCN, a model that combines large scale pretraining and transductive learning for text classification. BertGCN constructs a heterogeneous graph over the dataset and represents documents as nodes using BERT representations. By jointly training the BERT and GCN modules within BertGCN, 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. Code is available at https://github.com/ZeroRin/BertGCN.

Subfields: Artificial Intelligence

View Paper: https://www.semanticscholar.org/paper/d22b109eb5089179f8bd48ef47513533890f6bf9

Citations in the corpus

• ProtoSiTex: Learning Semi-Interpretable Prototypes for Multi-label Text Classification (2025, unknown): “We also compute Hamming loss ( HL ) to analyze label-wise prediction performance, and report complementary Hamming loss HL c here; HL c = (1 − HL ) × 100% .”