Improving Distant Supervised Relation Extraction by Dynamic Neural Network
Table 5: Accuracy (%) of Hits@K on relations with training instances fewer than 100/200.
['# Instances Hits@K', '<100 10', '<100 15', '<100 20', '<200 10', '<200 15', '<200 20']
PCNN+HATT Han et al. In particular, we observe that DNNRE achieve 57.6% and 64.1% accuracies for Hits@10. That is, more than half of true classes of the long-tail samples can be predicted into the first 10 candidate relation recommended by DNNRE, which outperforms other works significantly at least 20% absolute improvement. This demonstrates that DNNRE can substantially boost the performance of long-tail relation classes.