Papers › Text Level Graph Neural Network for Text Classification
Text Level Graph Neural Network for Text Classification
Lianzhe Huang, Dehong Ma, Sujian Li, Xiaodong Zhang, Houfeng Wang
Recently, researches have explored the graph neural network (GNN) techniques on text classification, since GNN does well in handling complex structures and preserving global information. However, previous methods based on GNN are mainly faced with the practical problems of fixed corpus level graph structure which do not support online testing and high memory consumption. To tackle the problems, we propose a new GNN based model that builds graphs for each input text with global parameters sharing instead of a single graph for the whole corpus. This method removes the burden of dependence between an individual text and entire corpus which support online testing, but still preserve global information. Besides, we build graphs by much smaller windows in the text, which not only extract more local features but also significantly reduce the edge numbers as well as memory consumption. Experiments show that our model outperforms existing models on several text classification datasets even with consuming less memory.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Text Classification | Ohsumed | Our Model* | Accuracy | 69.4 | #2 of 10 | Archive leaderboard | report |
| Text Classification | R52 | Our Model* | Accuracy | 94.6 | #3 of 8 | Archive leaderboard | report |
| Text Classification | R8 | Our Model* | Accuracy | 97.8 | #9 of 21 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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