Text Classification On Ohsumed
评估指标
Accuracy
评测结果
各个模型在此基准测试上的表现结果
模型名称 | Accuracy | Paper Title | Repository |
|---|---|---|---|
| SGC | 68.5 | Simplifying Graph Convolutional Networks | - |
| Text GCN | 68.36 | Graph Convolutional Networks for Text Classification | - |
| REL-RWMD k-NN | 58.74 | Speeding up Word Mover's Distance and its variants via properties of distances between embeddings | - |
| Our Model* | 69.4 | Text Level Graph Neural Network for Text Classification | - |
| SSGC | 68.5 | Simple Spectral Graph Convolution | |
| GraphStar | 64.2 | Graph Star Net for Generalized Multi-Task Learning | - |
| RoBERTaGCN | 72.8 | BertGCN: Transductive Text Classification by Combining GCN and BERT | - |
| SGCN | 68.5 | Simplifying Graph Convolutional Networks | - |
| ApproxRepSet | 64.06 | Rep the Set: Neural Networks for Learning Set Representations | - |
| CNN+Lowercased | 36.2 | On the Role of Text Preprocessing in Neural Network Architectures: An Evaluation Study on Text Categorization and Sentiment Analysis | - |
0 of 10 row(s) selected.