  We consider the challenging problem of entity typing over an extremely fine
grained set of types, wherein a single mention or entity can have many
simultaneous and often hierarchically-structured types. Despite the importance
of the problem, there is a relative lack of resources in the form of
fine-grained, deep type hierarchies aligned to existing knowledge bases. In
response, we introduce TypeNet, a dataset of entity types consisting of over
1941 types organized in a hierarchy, obtained by manually annotating a mapping
from 1081 Freebase types to WordNet. We also experiment with several models
comparable to state-of-the-art systems and explore techniques to incorporate a
structure loss on the hierarchy with the standard mention typing loss, as a
first step towards future research on this dataset.
