  How can we represent hierarchical information present in large type
inventories for entity typing? We study the ability of hyperbolic embeddings to
capture hierarchical relations between mentions in context and their target
types in a shared vector space. We evaluate on two datasets and investigate two
different techniques for creating a large hierarchical entity type inventory:
from an expert-generated ontology and by automatically mining type
co-occurrences. We find that the hyperbolic model yields improvements over its
Euclidean counterpart in some, but not all cases. Our analysis suggests that
the adequacy of this geometry depends on the granularity of the type inventory
and the way hierarchical relations are inferred.
