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;Mikolov et al., 2013a;Mikolov et al., 2013b;Levy et al., 2015).
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.