Models such as skip-gram (Mikolov et al., 2013a) and Glove (Pennington et al., 2014) capture the statistics of a large corpus and have good properties that corresponds to the semantics of words (Mikolov et al., 2013b).
there are certain problems with unsupervised word embeddings, such as the difficulty in modeling some fine-grained word semantics.