Joint Distant and Direct Supervision for Relation Extraction

Abstract

Supervised approaches to Relation Extrac-tion (RE) are characterized by higher ac-curacy than unsupervised models. Unfor-tunately, their applicability is limited by the need of training data for each rela-tion type. Automatic creation of such data using Distant Supervision (DS) provides a promising solution to the problem. In this paper, we study DS for designing end-to-end systems of sentence-level RE. In particular, we propose a joint model be-tween Web data derived with DS and man-ually annotated data from ACE. The re-sults show (i) an improvement on the pre-vious state-of-the-art in ACE, which pro-vides important evidence of the benefit of DS; and (ii) a rather good accuracy on ex-tracting 52 types of relations from Web data, which suggests the applicability of DS for general RE. 1