  Learning an embedding for a large collection of items is a popular approach
to overcome the computational limitations associated to one-hot encodings. The
aim of item embedding is to learn a low dimensional space for the
representations, able to capture with its geometry relevant features or
relationships for the data at hand. This can be achieved for example by
exploiting adjacencies among items in large sets of unlabelled data. In this
paper we interpret in an Information Geometric framework the item embeddings
obtained from conditional models. By exploiting the $\alpha$-geometry of the
exponential family, first introduced by Amari, we introduce a family of natural
$\alpha$-embeddings represented by vectors in the tangent space of the
probability simplex, which includes as a special case standard approaches
available in the literature. A typical example is given by word embeddings,
commonly used in natural language processing, such as Word2Vec and GloVe. In
our analysis, we show how the $\alpha$-deformation parameter can impact on
standard evaluation tasks.
