基于BoBGSAL⁃Net的文档级实体关系抽取方法
|
|
冯超文, 吴瑞刚, 温绍杰, 刘英莉
|
Document⁃level entity relation extraction method based on BoBGSAL⁃NET
|
|
Chaowen Feng, Ruigang Wu, Shaojie Wen, Yingli Liu
|
|
表3 BoBGSAL?Net模型和其他模型在DocRED数据集上的命名实体识别实验结果的对比
|
Table 3 Experimental results of named entity recognition by BoBGSAL?Net and other models on the DocRED dataset
|
|
| 模型 | 验证集 | 测试 |
|---|
| Ign F1 | Ign AUC | F1 | AUC | Ign F1 | F1 |
|---|
| BoBGSAL⁃Net | 54.33% | 53.75% | 55.84% | 54.97% | 54.14% | 55.08% | | CNN[12] | 41.58% | 36.85% | 43.45% | 39.39% | 40.33% | 42.26% | | LSTM[5] | 48.44% | 46.62% | 50.68% | 49.48% | 47.71% | 50.07% | | BiLSTM[6] | 48.87% | 47.61% | 50.94% | 50.26% | 48.78% | 51.06% | | Context⁃Aware[21] | 48.94% | 47.22% | 51.09% | 50.17% | 48.40% | 50.70% | | HIN⁃GloVe[7] | 51.06% | — | 52.95% | — | 51.15% | 53.30% | | CFER⁃GloVe[27] | 54.29% | — | 55.31% | — | 53.70% | 54.06% | | SSAN⁃BERT⁃base[28] | 54.03% | — | 54.95% | — | 53.44% | 53.16% | | GAIN+SIEF[29] | 53.82% | — | 54.24% | — | 53.87% | 54.79% |
|
|
|