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.
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. 11. The method of claim 1 wherein the posterior distribution over the corpus topics for the short message is determined by evaluating: k * = arg θ k ⁢ max ⁢ ⁢ p ⁡ ( k ) ⁢ ∏ i = 1 N ⁢ p ⁡ ( w i ′ ❘ k ) where k′ is a posterior distribution for a topic k, θ k denotes parameters for the k th Gaussian component of the Gaussian mixture model, w i ′ is the i th word in the sample short text message and the probabilities p(k) and p(w i ′|k) are obtained from the Gaussian mixture model.