Infusion of Labeled Data into Distant Supervision for Relation Extraction

Maria Pershina, Bonan Min, Wei Xu, Ralph Grishman

Abstract: Distant supervision usually utilizes only unlabeled data and existing knowledge bases to learn relation extraction models. However, in some cases a small amount of human labeled data is available. In this paper, we demonstrate how a state-of-theart multi-instance multi-label model can be modified to make use of these reliable sentence-level labels in addition to the relation-level distant supervision from a database. Experiments show that our approach achieves a statistically significant increase of 13.5% in F-score and 37% in area under the precision recall curve.