  A new development in NLP is the construction of hyperbolic word embeddings.
As opposed to their Euclidean counterparts, hyperbolic embeddings are
represented not by vectors, but by points in hyperbolic space. This makes the
most common basic scheme for constructing document representations, namely the
averaging of word vectors, meaningless in the hyperbolic setting. We
reinterpret the vector mean as the centroid of the points represented by the
vectors, and investigate various hyperbolic centroid schemes and their
effectiveness at text classification.
