  Word embeddings are real-valued word representations able to capture lexical
semantics and trained on natural language corpora. Models proposing these
representations have gained popularity in the recent years, but the issue of
the most adequate evaluation method still remains open. This paper presents an
extensive overview of the field of word embeddings evaluation, highlighting
main problems and proposing a typology of approaches to evaluation, summarizing
16 intrinsic methods and 12 extrinsic methods. I describe both widely-used and
experimental methods, systematize information about evaluation datasets and
discuss some key challenges.
