We observe that, on human-annotated dataset, neural relation extraction models outperform feature-based models by notable gaps, but these gaps diminish when the same models are applied to DS datasets-neural models merely achieve performance comparable with feature-based models.
in the train set of KBP, 74.25% instances are annotated as NONE and 85.67% in the test set.