Knowledge-embodied attention for distantly supervised relation extraction
Kejun Deng, Xuemiao Zhang, Songtao Ye, Junfei Liu · Intelligent Data Analysis · 2020
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 b ackground 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 sentence-level 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.