Predicting Guiding Entities for Entity Aspect Linking

Shubham Chatterjee, Laura Dietz

Abstract: Entity linking can disambiguate mentions of an entity in text. However, there are many different aspects of an entity that could be discussed but are not differentiable by entity links, for example, the entity "oyster'' in the context of "food'' or "ecosystems''. Entity aspect linking provides such fine-grained explicit semantics for entity links by identifying the most relevant aspect of an entity in the given context. We propose a novel entity aspect linking approach that outperforms several neural and non-neural baselines on a large-scale entity aspect linking test collection. Our approach uses a supervised neural entity ranking system to predict relevant entities for the context. These entities are then used to guide the system to the correct aspect.