Models like Skip-gram, CBOW (Mikolov et al., 2013a) and GloVe (Pennington et al., 2014) are immensely popular and achieve remarkable performance in many NLP tasks.
most WRL methods learn distributional information of words from large corpora while the valuable information contained in semantic lexicons are disregarded.