Effective Attention Modeling for Neural Relation Extraction
Table 2: Performance comparison of different models on the two datasets. * denotes a statistically significant improvement over the previous best state-of-the-art model with p<0.01 under the bootstrap paired t-test. † denotes the previous best state-of-the-art model.
['Model', 'NYT10 Prec.', 'NYT10 Rec.', 'NYT10 F1', 'NYT11 Prec.', 'NYT11 Rec.', 'NYT11 F1']
We present the results of our final model on the relation extraction task on the two datasets in Table 2. Our model outperforms the previous stateof-the-art models on both datasets in terms of F1 score. On the NYT10 dataset, it achieves 4.3% higher F1 score compared to the previous best state-of-the-art model EA. Similarly, it achieves 3.3% higher F1 score compared to the previous best state-of-the-model PCNN on the NYT11 dataset. Our model improves the precision scores on both datasets with good recall scores.