Autoencoders (Kingma and Welling, 2014;Vincent et al., 2008) have gained popularity as a method for learning feature representations from unlabelled data that can then be used for supervised learning tasks.
to sparse and high-dimensional counting-based distributional word representation methods that use co-occurring contexts of a word as its representation, dense and low-dimensional prediction-based distributed word representations (Pennington et al., 2014;Mikolov et al., 2013a;Huang et al., 2012;Collobert and Weston, 2008;Mnih and Hinton, 2009) have obtained impressive performances in numerous NLP tasks such as sentiment classification , and machine translation (Zou et al., 2013).