  The computational study of lexical semantic change (LSC) has taken off in the
past few years and we are seeing increasing interest in the field, from both
computational sciences and linguistics. Most of the research so far has focused
on methods for modelling and detecting semantic change using large diachronic
textual data, with the majority of the approaches employing neural embeddings.
While methods that offer easy modelling of diachronic text are one of the main
reasons for the spiking interest in LSC, neural models leave many aspects of
the problem unsolved. The field has several open and complex challenges. In
this chapter, we aim to describe the most important of these challenges and
outline future directions.
