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Applications of deep belief nets (DBN) to various problems have been the subject of a number of recent studies ranging from image classification and speech recognition to audio classification. In this study we apply DBNs to a natural language understanding problem. The recent surge of activity in this area was largely spurred by the development of a greedy layer-wise pretraining method that uses an efficient learning algorithm called contrastive divergence (CD). CD allows DBNs to learn adoi:10.1109/taslp.2014.2303296 fatcat:abcie3caqvgu7lspzbfv2m6xfq