Research Article
Multihop Neighbor Information Fusion Graph Convolutional Network for Text Classification
Table 4
Test accuracy on five text datasets. The benchmark results were reported by Yao et al. [
15]. The accuracy values are the average result of 10 runs.
| Method | R52 | R8 | 20NG | Ohsumed | MR |
| CNN-rand [39] | 85.37 | 94.02 | 76.93 | 43.87 | 74.98 | CNN-pretrain [39] | 87.59 | 95.71 | 82.15 | 58.44 | 77.75 | PTE [46] | 90.71 | 96.69 | 76.74 | 53.58 | 70.23 | LSTM [2] | 85.54 | 93.68 | 65.71 | 41.13 | 75.06 | LSTM-pretrain [2] | 90.48 | 96.09 | 75.43 | 51.10 | 77.33 | fastText [47] | 92.81 | 96.13 | 79.38 | 57.70 | 75.14 | fastText-bigrams [47] | 90.99 | 94.74 | 79.67 | 55.69 | 76.24 | LEAM [48] | 91.84 | 93.31 | 81.91 | 58.58 | 76.95 | SWEM [49] | 92.94 | 95.32 | 85.16 | 63.12 | 76.65 | GCNN-S [23] | 92.74 | 96.80 | — | 62.82 | 76.99 | GCNN-F [50] | 93.20 | 96.89 | — | 63.04 | 76.74 | GCNN-C [10] | 92.75 | 96.99 | 81.42 | 63.86 | 77.22 | Text GCN [15] | 93.56 ± 0.18 | 97.07 ± 0.10 | 86.34 ± 0.09 | 68.36 ± 0.56 | 76.74 ± 0.20 | NMGC-2 (ours) | 94.35 ± 0.06 | 97.31 ± 0.09 | 86.61 ± 0.06 | 69.21 ± 0.17 | 76.21 ± 0.25 | NMGC-3 (ours) | 93.83 ± 0.16 | 97.16 ± 0.10 | 86.68 ± 0.18 | 68.20 ± 0.35 | 76.36 ± 0.40 |
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