  Recent years have witnessed a surge of publications aimed at tracing temporal
changes in lexical semantics using distributional methods, particularly
prediction-based word embedding models. However, this vein of research lacks
the cohesion, common terminology and shared practices of more established areas
of natural language processing. In this paper, we survey the current state of
academic research related to diachronic word embeddings and semantic shifts
detection. We start with discussing the notion of semantic shifts, and then
continue with an overview of the existing methods for tracing such time-related
shifts with word embedding models. We propose several axes along which these
methods can be compared, and outline the main challenges before this emerging
subfield of NLP, as well as prospects and possible applications.
