Embeddings in general and Word embeddings, in particular, are real-valued entity representations able to capture context semantics and trained on unique corpora. Models proposing these representations have gained popularity in recent years, but the issue of the most adequate evaluation method still remains open. I will present an extensive overview of the field of words and non-NLP embeddings evaluation, highlighting main problems and proposing a typology of approaches to evaluation.