Table 2.
Performance of RnRTD methods based on multiple deep neural networks.
| Optimization method | Basic algorithm | ||||||
|---|---|---|---|---|---|---|---|
| TextCNN | TextRNN | TextCNN attention | TextRNN attention | LSTM | Bi-LSTM | GRU | |
| Original method | 0.70 | 0.73 | 0.74 | 0.76 | 0.75 | 0.77 | 0.79 |
| Only with RTD | 0.73 | 0.77 | 0.79 | 0.82 | 0.80 | 0.81 | 0.83 |
| Only with rdRNN | 0.74 | 0.80 | 0.82 | 0.83 | 0.83 | 0.84 | 0.82 |
| With RnRTD | 0.80 | 0.85 | 0.85 | 0.90 | 0.91 | 0.93 | 0.91 |