Word embeddings are obtained from an unsupervised learning model that learns co-occurrence statistics of words from a large external corpus, yielding word embeddings as distributional semantics (Mikolov et al., 2013).
we use pre-trained embeddings from GloVE (Pennington et al., 2014).