Diachronic word embeddings, i.e., high-dimensional time-dependent embeddings of textual information, can be used to reveal shifts of semantic word meanings over time. For example, diachronic word embeddings could reveal that the German word “Widerstand” changed its primary meaning from an electrical context to resistance to Nazism in the last century . Word embeddings can be visualized by using dimensionality reduction (see, for example, Tensorflow’s Embedding Projector ), but such visualizations typically do not provide an expressive overview and do not reveal semantic changes over time.
The task of this thesis will be to design, implement, and validate a comparative visualization  to support interactive exploration of semantic word shifts uncovered by diachronic word embeddings over time. Providing an expressive and scalable overview of changes is highly challenging, as such diachronic studies are usually based on tens of thousands to millions of words.