The prevailing methods for the computation of a vector space representation are based on distributional semantics (Harris, 1954).
these approaches, whether in their conventional co-occurrence based form (Salton et al., 1975;Turney and Pantel, 2010;Landauer and Dooley, 2002), or in their newer predictive branch (Collobert and Weston, 2008;Mikolov et al., 2013;Baroni et al., 2014), suffer from a major drawback: they are unable to model individual word senses or concepts, as they conflate different meanings of a word into a single vectorial representation.