  While many methods for learning vector space embeddings have been proposed in
the field of Natural Language Processing, these methods typically do not
distinguish between categories and individuals. Intuitively, if individuals are
represented as vectors, we can think of categories as (soft) regions in the
embedding space. Unfortunately, meaningful regions can be difficult to
estimate, especially since we often have few examples of individuals that
belong to a given category. To address this issue, we rely on the fact that
different categories are often highly interdependent. In particular, categories
often have conceptual neighbors, which are disjoint from but closely related to
the given category (e.g.\ fruit and vegetable). Our hypothesis is that more
accurate category representations can be learned by relying on the assumption
that the regions representing such conceptual neighbors should be adjacent in
the embedding space. We propose a simple method for identifying conceptual
neighbors and then show that incorporating these conceptual neighbors indeed
leads to more accurate region based representations.
