Denoising Distant Supervision for Relation Extraction via Instance-Level Adversarial Training
Table 3: Top-N precision (P@N) for RE in the entity pairs with different number of instances (%).
['Test Settings P@N', 'One 100', 'One 200', 'One 300', 'One Mean', 'Two 100', 'Two 200', 'Two 300', 'Two Mean', 'All 100', 'All 200', 'All 300', 'All Mean']
For each entity pair, we randomly select one sentence, two sentences, and all sentences to construct three experimental settings respectively. We report P@100, P@200, P@300 and the mean of them in the held-out evaluation. Since PCNN is the best neural model in the above comparison, we simply use PCNN to compare our method (AN) with the recent state-of-the-art denoising method, sentence-level attention (ATT), as well as its naive versions + ONE and +AVG Zeng et al.