Distant Supervision via Prototype-Based Global Representation Learning

Abstract

Distant supervision (DS) is a promising technique for relation extraction. Currently, most DS approaches build relation extraction models in local instance feature space, often suffer from the multi-instance problem and the missing label problem. In this paper, we propose a new DS method — prototype-based global representation learning, which can effectively resolve the multi-instance problem and the missing label problem by learning informative entity pair representations, and building discriminative extraction models at the entity pair level, rather than at the instance level. Specifically, we propose a prototype-based embedding algorithm, which can embed entity pairs into a prototype-based global feature space; we then propose a neural network model, which can classify entity pairs into target relation types by summarizing relevant information from multiple instances. Experimental results show that our method can achieve significant performance improvement over traditional DS methods.