Short text clustering has become an increasing important task with the popularity of social media, and it is a challenging problem due to its sparseness of text representation. In this paper, we propose a Short Text Clustering via Convolutional neural networks (abbr. to STCC), which is more beneficial for clustering by considering one constraint on learned features through a self-taught learning framework without using any external tags/labels. First, we embed the original keyword features into compact binary codes with a locality-preserving constraint. Then, word embed-dings are explored and fed into convolutional neural networks to learn deep feature representations, with the output units fitting the pre-trained binary code in the training process. After obtaining the learned representations, we use K-means to cluster them. Our extensive experimental study on two public short text datasets shows that the deep feature representation learned by our approach can achieve a significantly better performance than some other existing features, such as term frequency-inverse document frequency, Laplacian eigenvectors and average embedding, for clustering.
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