  In this work, we propose a semi-supervised method for short text clustering,
where we represent texts as distributed vectors with neural networks, and use a
small amount of labeled data to specify our intention for clustering. We design
a novel objective to combine the representation learning process and the
k-means clustering process together, and optimize the objective with both
labeled data and unlabeled data iteratively until convergence through three
steps: (1) assign each short text to its nearest centroid based on its
representation from the current neural networks; (2) re-estimate the cluster
centroids based on cluster assignments from step (1); (3) update neural
networks according to the objective by keeping centroids and cluster
assignments fixed. Experimental results on four datasets show that our method
works significantly better than several other text clustering methods.
