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Entity embeddings, which represent different aspects of each entity with asingle vector like word embeddings, are a key component of neural entitylinking models. Existing entity embeddings are learned from canonical Wikipediaarticles and local contexts surrounding target entities. Such entity embeddingsare effective, but too distinctive for linking models to learn contextualcommonality. We propose a simple yet effective method, FGS2EE, to injectfine-grained semantic information into entity embeddings to reduce thedistinctiveness and facilitate the learning of contextual commonality. FGS2EEfirst uses the embeddings of semantic type words to generate semanticembeddings, and then combines them with existing entity embeddings throughlinear aggregation. Extensive experiments show the effectiveness of suchembeddings. Based on our entity embeddings, we achieved new sate-of-the-artperformance on entity linking.

Improving Entity Linking through Semantic Reinforced Entity Embeddings