Continuous space models of words, objects, and signals have become ubiquitous tools for learning rich representations of data, from natural language processing to computer vision.
there has been particular interest in word embeddings, largely due to their intriguing semantic properties (Mikolov et al., 2013b) and their success as features for downstream natural language processing tasks, such as named entity recognition (Turian et al., 2010) and parsing (Socher et al., 2013).