  The Word-in-Context (WiC) task has attracted considerable attention in the
NLP community, as demonstrated by the popularity of the recent MCL-WiC SemEval
shared task. Systems and lexical resources from word sense disambiguation (WSD)
are often used for the WiC task and WiC dataset construction. In this paper, we
establish the exact relationship between WiC and WSD, as well as the related
task of target sense verification (TSV). Building upon a novel hypothesis on
the equivalence of sense and meaning distinctions, we demonstrate through the
application of tools from theoretical computer science that these three
semantic classification problems can be pairwise reduced to each other, and
therefore are equivalent. The results of experiments that involve systems and
datasets for both WiC and WSD provide strong empirical evidence that our
problem reductions work in practice.
