Deep Residual Learning for Weakly-Supervised Relation Extraction
Table 2: P@N for relation extraction with different models. Top: models that select training data. Bottom: models without selective attention.
['P@N(%)', '100', '200', '300', 'Mean']
We show that our ResCNN-9 outperforms all models that do not select training instances. And even without piecewise max-pooling and instance-based attention, our model is on par with the PCNN+ATT model.