Distant supervised relation extraction with position feature attention and selective bag attention

Abstract Distant supervision greatly reduces manual consumption by automatically labeling data. The relation extraction methods under distant supervision divide sentences with the same entity pair into a bag, and perform training and testing on these sentence bags. The existing distant supervised relation extraction methods ignore two facts. First, there are many sentences where the target entity pairs appear multiple times. Second, the noise between sentence bags is different, and the sentences of some bags are even all mislabeled. To solve these two problems, we propose a novel relation extraction method with position feature attention and selective bag attention. The position feature attention is employed to obtain the weighted sentence representation with different position features by calculating all position combinations of the target entity pair. A bag with large noise and a bag with small noise are selected through the selective bag attention mechanism to form a bag pair, and training is performed at the level of the bag pair, which denoises at the bag level and at the same time balances the noise between different bag pairs. The experimental results show that our method is effective and outperforms several competitive baseline methods.