In this paper, we propose a new distant supervision method, called Semantic Consistency, which can identify reliable instances from noisy instances by inspecting whether an instance is located in a semantically consistent region.
we propose a semantic consistency model, which first models the local subspace around an instance as a sparse linear combination of training instances, then estimate the semantic consistency by exploiting the characteristics of the local subspace.