# Semi-supervised Clustering for Short Text via Deep Representation Learning

arXiv: https://arxiv.org/abs/1602.06797 · HTML: https://mlsanity.com/paper/1602.06797/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Feb 2016.

## Built on by (most cited first, 5 of 5)

- [Cluster & Tune: Boost Cold Start Performance in Text Classification](https://mlsanity.com/paper/2203.10581/) (2022)
- [NeurCAM: Interpretable Neural Clustering via Additive Models](https://mlsanity.com/paper/2408.13361/) (2024)
- [Dialog Intent Induction via Density-based Deep Clustering Ensemble](https://mlsanity.com/paper/2201.06731/) (2022)
- [A Semi-Supervised Deep Clustering Pipeline for Mining Intentions From Texts](https://mlsanity.com/paper/2202.00802/) (2022)
- [An Adaptive Deep Clustering Pipeline to Inform Text Labeling at Scale](https://mlsanity.com/paper/2202.01211/) (2022)

## Abstract (arXiv)

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.