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Proceedings of the 28th International Conference on Computational Linguistics
Distantly supervised relation extraction has been widely applied in knowledge base construction due to its less requirement of human efforts. However, the automatically established training datasets in distant supervision contain low-quality instances with noisy words and overlapped relations, introducing great challenges to the accurate extraction of relations. To address this problem, we propose a novel Regularized Attentive Capsule Network (RA-CapNet) to better identify highly overlappeddoi:10.18653/v1/2020.coling-main.562 fatcat:643aawma6bfelgiysaywb6osga