Semantic Consistency: A Local Subspace Based Method for Distant Supervised Relation Extraction

Abstract

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… (More)

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Cite this paper

@inproceedings{Han2014SemanticCA, title={Semantic Consistency: A Local Subspace Based Method for Distant Supervised Relation Extraction}, author={Xianpei Han and Le Sun}, booktitle={ACL}, year={2014} }