  In computational linguistics, a large body of work exists on distributed
modeling of lexical relations, focussing largely on lexical relations such as
hypernymy (scientist -- person) that hold between two categories, as expressed
by common nouns. In contrast, computational linguistics has paid little
attention to entities denoted by proper nouns (Marie Curie, Mumbai, ...). These
have investigated in detail by the Knowledge Representation and Semantic Web
communities, but generally not with regard to their linguistic properties.
  Our paper closes this gap by investigating and modeling the lexical relation
of instantiation, which holds between an entity-denoting and a
category-denoting expression (Marie Curie -- scientist or Mumbai -- city). We
present a new, principled dataset for the task of instantiation detection as
well as experiments and analyses on this dataset. We obtain the following
results: (a), entities belonging to one category form a region in
distributional space, but the embedding for the category word is typically
located outside this subspace; (b) it is easy to learn to distinguish entities
from categories from distributional evidence, but due to (a), instantiation
proper is much harder to learn when using common nouns as representations of
categories; (c) this problem can be alleviated by using category
representations based on entity rather than category word embeddings.
