Research Article
Combining BERT Model with Semi-Supervised Incremental Learning for Heterogeneous Knowledge Fusion of High-Speed Railway On-Board System
Table 2
Experimental results under different EM models.
| ā | Entity representation | Models | Marco-P | Marco-R | Marco-F |
| I | Embedding-based models | ABCNN-3 | 0.8782 | 0.8762 | 0.8765 | LSTM-Siamese | 0.8553 | 0.8219 | 0.8374 | BIMPM | 0.9078 | 0.8998 | 0.8985 | ESIM | 0.9127 | 0.9093 | 0.9086 |
| II | BERT-based models | ALBERT | 0.9553 | 0.9511 | 0.9529 | ERNIE | 0.9729 | 0.9711 | 0.9720 | III | AD--SSI | 0.9887 | 0.9883 | 0.9885 | AD--SSI | 0.9859 | 0.9856 | 0.9857 | AD--SSI | 0.9871 | 0.9863 | 0.9867 |
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