Semantic Consistency: A Local Subspace Based Method for Distant Supervised Relation Extraction
Xianpei Han, Le Sun · 2014
One fundamental problem of distant supervision is the noisy training corpus problem.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.Specifically, 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.Experimental results verified the effectiveness of our method.