  The problem of representing the atomic elements of language in modern neural
learning systems is one of the central challenges of the field of natural
language processing. I present a survey of the distributional, compositional,
and relational approaches to addressing this task, and discuss various means of
integrating them into systems, with special emphasis on the word level and the
out-of-vocabulary phenomenon.
