Distant Supervision for Relation Extraction with Neural Instance Selector

Abstract

Distant supervised relation extraction is an efficient method to find novel relational facts from very large corpora without expensive manual annotation. However, distant supervision will inevitably lead to wrong label problem, and these noisy labels will substantially hurt the performance of relation extraction. Existing methods usually use multi-instance learning and selective attention to reduce the influence of noise. However, they usually cannot fully utilize the supervision information and eliminate the effect of noise. In this paper, we propose a method called Neural Instance Selector (NIS) to solve these problems. Our approach contains three modules, a sentence encoder to encode input texts into hidden vector representations, an NIS module to filter the less informative sentences via multilayer perceptrons and logistic classification, and a selective attention module to select the important sentences. Experimental results show that our method can effectively filter noisy data and achieve better performance than several baseline methods.