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Distant supervised relation extraction (RE) has been an effective way of finding novel relational facts from text without labeled training data. Typically it can be formalized as a multi-instance multi-label problem.In this paper, we introduce a novel neural approach for distant supervised (RE) with specific focus on attention mechanisms.Unlike the feature-based logistic regression model and compositional neural models such as CNN, our approach includes two major attention-based memorydoi:10.24963/ijcai.2017/559 dblp:conf/ijcai/FengGQLL17 fatcat:d5ueqv6ktrbopo4jq4jamft5sq