Relevance-Based Entity Embedding

Weixin Zeng, Xiang Zhao, Jiuyang Tang, Jinzhi Liao, Chang-Dong Wang

Abstract: Entity embedding plays an indispensable role in many entity-related problems. Currently, mainstream entity embedding methods build on the notion that entities with similar contexts or close proximity should be placed adjacently in the embedding space. Nonetheless, this goal fails to meet the objectives of many downstream tasks, where the relevance among entities is more significant. To fill this gap, in this paper, a novel relevance-based entity embedding approach, Lead, is proposed, where the relevance is captured via query-document information. The experimental results verify the superiority of our proposal.