There is an accumulation of evidence that the use of dense distributional lexical representations, known as word embeddings, often supports better performance on a range of NLP tasks (Bengio et al., 2003; Turian et al., 2010; Collobert et al., 2011; <cite>Mikolov et al., 2013a</cite>; Mikolov et al., 2013b; Levy et al., 2015) . Consequently, word embeddings have been commonly used in the last few years for lexical similarity tasks and as features in multiple, syntactic and semantic, NLP applications.