Semi-supervised Clustering for Short Text via Deep Representation Learning
This addresses the challenge of clustering short texts with limited labeled data, which is incremental as it builds on existing semi-supervised and representation learning approaches.
The paper tackles the problem of short text clustering by proposing a semi-supervised method that uses neural networks for representation learning and integrates it with k-means clustering, showing significantly better performance than other methods on four datasets.
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.