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Distant supervision significantly reduces human efforts in building training data for many classification tasks. While promising, this technique often introduces noise to the generated training data, which can severely affect the model performance. In this paper, we take a deep look at the application of distant supervision in relation extraction. We show that the dynamic transition matrix can effectively characterize the noise in the training data built by distant supervision. The transitiondoi:10.18653/v1/p17-1040 dblp:conf/acl/LuoFWZHYZ17 fatcat:qdmtt7oftvbgfnyumlb3kl6ctq