  The word embedding methods have been proven to be very useful in many tasks
of NLP (Natural Language Processing). Much has been investigated about word
embeddings of English words and phrases, but only little attention has been
dedicated to other languages.
  Our goal in this paper is to explore the behavior of state-of-the-art word
embedding methods on Czech, the language that is characterized by very rich
morphology. We introduce new corpus for word analogy task that inspects
syntactic, morphosyntactic and semantic properties of Czech words and phrases.
We experiment with Word2Vec and GloVe algorithms and discuss the results on
this corpus. The corpus is available for the research community.
