Entity-Aspect Linking: Providing Fine-Grained Semantics of Entities in Context

The availability of entity linking technologies provides a novel way to organize, categorize, and analyze large textual collections in digital libraries. However, in many situations a link to an entity offers only relatively coarse-grained semantic information. This is problematic especially when the entity is related to several different events, topics, roles, and - more generally - when it has different aspects. In this work, we introduce and address the task of entity-aspect linking: given a mention of an entity in a contextual passage, we refine the entity link with respect to the aspect of the entity it refers to. We show that a combination of different features and aspect representations in a learning-to-rank setting correctly predicts the entity-aspect in 70% of the cases. Additionally, we demonstrate significant and consistent improvements using entity-aspect linking on three entity prediction and categorization tasks relevant for the digital library community.