Entity retrieval, which aims at disambiguating mentions to canonical entities from massive KBs, is essential for many tasks in natural language processing. Recent progress in entity retrieval shows that the dual-encoder structure is a powerful and efficient framework to nominate candidates if entities are only identified by descriptions. However, they ignore the property that meanings of entity mentions diverge in different contexts and are related to various portions of descriptions, which are treated equally in previous works. In this work, we propose Multi-View Entity Representations (MuVER), a novel approach for entity retrieval that constructs multi-view representations for entity descriptions and approximates the optimal view for mentions via a heuristic searching method. Our method achieves the state-of-the-art performance on ZESHEL and improves the quality of candidates on three standard Entity Linking datasets

该研究提出了一种新方法，称为多视图实体表示（MuVER），旨在提高实体检索的性能，通过启发式搜索方法构造实体描述的多视图表示，并通过这种方式提高候选实体的质量。该方法在ZESHEL上取得了最新的性能，并在三个标准实体链接数据集上改善了候选者的质量。

MuVER: 多视角实体表示优化第一阶段实体检索