Paper: Type-Aware Distantly Supervised Relation Extraction with Linked Arguments

ACL ID D14-1203
Title Type-Aware Distantly Supervised Relation Extraction with Linked Arguments
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2014
Authors

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 relati...