In principle, this approach is similar to knowledge-based approaches for word sense disambiguation, especially the classic algorithm Lesk and its extensions (Lesk, 1986;Agirre et al., 2014;Basile et al., 2014), that apply a common strategy to disambiguate any word by referring to sense representations and measuring overlaps between a word context and sense representations.
different from the previous knowledge-based approaches that  directly use sense representations provided by lexicalsemantic resources, we leverage sense-annotated corpus as well in a data-driven manner to learn to build sense representations and learn to measure the overlap between a word context and a sense representation.