  We present Sense Clustering over Time (SCoT), a novel network-based tool for
analysing lexical change. SCoT represents the meanings of a word as clusters of
similar words. It visualises their formation, change, and demise. There are two
main approaches to the exploration of dynamic networks: the discrete one
compares a series of clustered graphs from separate points in time. The
continuous one analyses the changes of one dynamic network over a time-span.
SCoT offers a new hybrid solution. First, it aggregates time-stamped documents
into intervals and calculates one sense graph per discrete interval. Then, it
merges the static graphs to a new type of dynamic semantic neighbourhood graph
over time. The resulting sense clusters offer uniquely detailed insights into
lexical change over continuous intervals with model transparency and
provenance. SCoT has been successfully used in a European study on the changing
meaning of `crisis'.
