9. The method of claim 1 wherein estimating the plurality of Gaussian components further comprises estimating means, covariances and mixture weights for each Gaussian component using an expectation-maximization algorithm.
1. A method for determining topics of short text messages, comprising: by a computer, obtaining distributed vector representations of words in a vocabulary identified in a corpus comprising a plurality of training short text messages, the distributed vector representations being obtained by processing windows of the corpus having a context window fixed length; by the computer, estimating a plurality of Gaussian components of a Gaussian mixture model of the corpus using the distributed vector representations and using bottleneck features obtained using neural networks, the Gaussian components representing corpus topics; by the computer, receiving a sample short text message comprising a subset of the words in the vocabulary; and by the computer, determining a topic of the sample short text message based on a posterior distribution over the corpus topics for the sample short text message, the posterior distribution obtained using the Gaussian mixture model.