Knowledge-embodied attention for distantly supervised relation extraction

Knowledge bases (KBs) provide a large amount of structured information for entities and relations, which are successfully leveraged in many natural language processing tasks. However, distantly supervised relation extraction only utilizes KBs to automatically generate datasets, while ignoring the background information in KBs during the relation extraction process. We herein propose a knowledge-embodied attention that leverages knowledge information in KBs to reduce the impact of noisy data for distantly supervised relation extraction. Specifically, we pre-train distributed representations of KBs with the knowledge representation learning (KRL) model, and subsequently incorporate them into relation extraction to learn sentencelevel attention weights. The experimental results demonstrate that our approach outperforms all baselines, thus indicating that we can focus our attention on valid data by leveraging background information in KBs.