Short Text Clustering via Convolutional Neural Networks

This is the authors' abstract. We don't add key points for this paper.

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 localitypreserving constraint. Then, word embeddings 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.