  There is abundant evidence of the fact that the way words change their
meaning can be classified in different types of change, highlighting the
relationship between the old and new meanings (among which generalization,
specialization and co-hyponymy transfer). In this paper, we present a way of
detecting these types of change by constructing a model that leverages
information both from synchronic lexical relations and definitions of word
meanings. Specifically, we use synset definitions and hierarchy information
from WordNet and test it on a digitized version of Blank's (1997) dataset of
semantic change types. Finally, we show how the sense relationships can improve
models for both approximation of human judgments of semantic relatedness as
well as binary Lexical Semantic Change Detection.
