Effective Attention Modeling for Neural Relation Extraction
Table 3: Performance comparison of our model with different values of m on the two datasets.
['[ITALIC] m', 'NYT10 Prec.', 'NYT10 Rec.', 'NYT10 F1', 'NYT11 Prec.', 'NYT11 Rec.', 'NYT11 F1']
We investigate the effects of the multi-factor count (m) in our final model on the test datasets in Table 3. We observe that for the NYT10 dataset, m = {1, 2, 3} gives good performance with m = 1 achieving the highest F1 score. On the NYT11 dataset, m = 4 gives the best performance. These experiments show that the number of factors giving the best performance may vary depending on the underlying data distribution.