Entity embeddings, which represent different aspects of each entity with a
single vector like word embeddings, are a key component of neural entity
linking models. Existing entity embeddings are learned from canonical Wikipedia
articles and local contexts surrounding target entities. Such entity embeddings
are effective, but too distinctive for linking models to learn contextual
commonality. We propose a simple yet effective method, FGS2EE, to inject
fine-grained semantic information into entity embeddings to reduce the
distinctiveness and facilitate the learning of contextual commonality. FGS2EE
first uses the embeddings of semantic type words to generate semantic
embeddings, and then combines them with existing entity embeddings through
linear aggregation. Extensive experiments show the effectiveness of such
embeddings. Based on our entity embeddings, we achieved new sate-of-the-art
performance on entity linking.

文章提出了简单且有效的 FGS2EE 方法，通过将语义类型词的嵌入与现有实体嵌入进行线性聚合，注入了细粒度的语义信息进入实体嵌入，以减少其鲜明性并便于上下文语境学习。实验证明，这种嵌入方法在实体链接任务中表现优于先前的方法，并取得了新的最佳效果。