Paper: Infusion of Labeled Data into Distant Supervision for Relation Extraction

ACL ID P14-2119
Title Infusion of Labeled Data into Distant Supervision for Relation Extraction
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2014
Authors

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-the- art multi-instance multi-label model can be modified to make use of these reli- able sentence-level labels in addition to the relation-level distant supervision from a database. Experiments show that our ap- proach achieves a statistically significant increase of 13.5% in F-score and 37% in area under the precision recall curve.