Type-Aware Distantly Supervised Relation Extraction with Linked Arguments

Abstract

Distant supervision has become the lead-ing method for training large-scale rela-tion extractors, with nearly universal adop-tion in recent TAC knowledge-base pop-ulation competitions. However, there are still many questions about the best way to learn such extractors. In this paper we investigate four orthogonal improvements: integrating named entity linking (NEL) and coreference resolution into argument identification for training and extraction, enforcing type constraints of linked argu-ments, and partitioning the model by rela-tion type signature. We evaluate sentential extraction perfor-mance on two datasets: the popular set of NY Times articles partially annotated by Hoffmann et al. (2011) and a new dataset, called GORECO, that is comprehensively annotated for 48 common relations. We find that using NEL for argument identi-fication boosts performance over the tra-ditional approach (named entity recogni-tion with string match), and there is further improvement from using argument types. Our best system boosts precision by 44% and recall by 70%. 1