Knowledge-aware and retrieval-based models for distantly supervised relation extraction

Abstract

Distantly supervised relation extraction (RE) has been an effective way to find novel relational facts from text without a large amount of well-labeled training data. However, distant supervision always suffers from wrong labelling problem. Many neural approaches have been proposed to alleviate this problem recently, but none of them can make use of the rich semantic knowledge in the knowledge bases (KBs). In this paper, we propose a knowledge-aware attention model, which can leverage the semantic knowledge in the KB to select the valid sentences. Furthermore, based on knowledge representation learning (KRL), we formalize distantly supervised RE as relation retrieval instead of relation classification to leverage the semantic knowledge further. Experimental results on widely used datasets show that our approaches significantly outperform the popular benchmark methods.