BNET-DS

Abstract: Distant Supervision (DS) is a popular technique for developing relation extractors starting with limited supervision. Our contributions in this paper are threefold. Firstly, we propose three novel models for distantly-supervised relation extraction: (1) a Bi-GRU based word attention model (BGWA), (2) an entity-centric attention model (EA), and (3) and a combination model (BNET-DS) which jointly trains and combines multiple complementary models for improved relation extraction. Secondly, we introduce GDS, a new distant supervision dataset for relation extraction. GDS removes test data noise present in all previous distance supervision benchmark datasets, making credible automatic evaluation possible. Thirdly, through extensive experiments on multiple real-world datasets, we demonstrate effectiveness of the proposed methods.