基于BoBGSAL⁃Net的文档级实体关系抽取方法
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冯超文, 吴瑞刚, 温绍杰, 刘英莉
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Document⁃level entity relation extraction method based on BoBGSAL⁃NET
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Chaowen Feng, Ruigang Wu, Shaojie Wen, Yingli Liu
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表4 BoBGSAL?Net模型和其他模型在AlSiaRED数据集上的命名实体识别实验结果的对比
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Table 4 Experimental results of named entity recognition by BoBGSAL?Net and other models on the AlSiaRED dataset
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| 模型 | 验证集 | 测试 |
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| Ign F1 | Ign AUC | F1 | AUC | Ign F1 | F1 |
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| BoBGSAL⁃Net | 53.66% | 53.19% | 55.39% | 55.23% | 52.55% | 54.83% | | CNN[12] | 39.53% | 31.47% | 40.15% | 32.44% | 38.73% | 39.20% | | LSTM[5] | 41.34% | 40.43% | 43.03% | 41.09% | 41.26% | 42.97% | | BiLSTM[6] | 44.08% | 43.65% | 46.57% | 45.13% | 43.24% | 45.16% | | Context⁃Aware[21] | 46.09% | 45.36% | 48.85% | 47.33% | 46.13% | 48.17% | | HIN⁃GloVe[7] | 48.38% | — | 50.35% | — | 48.24% | 50.18% | | CFER⁃GloVe[27] | 53.34% | — | 54.27% | — | 52.45% | 53.60% | | SSAN⁃BERT⁃base[28] | 53.45% | — | 53.25% | — | 52.34% | 53.27% | | GAIN+SIEF[29] | 53.82% | — | 54.24% | — | 53.87% | 53.29% |
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