# Enhancement of Short Text Clustering by Iterative Classification

arXiv: https://arxiv.org/abs/2001.11631 · HTML: https://mlsanity.com/paper/2001.11631/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Jan 2020.

## Built on by (most cited first, 16 of 16)

- [Supporting Clustering with Contrastive Learning](https://mlsanity.com/paper/2103.12953/) (2021)
- [Twin Contrastive Learning for Online Clustering](https://mlsanity.com/paper/2210.11680/) (2022)
- [Mitigating Data Sparsity for Short Text Topic Modeling by Topic-Semantic Contrastive Learning](https://mlsanity.com/paper/2211.12878/) (2022)
- [Pairwise Supervised Contrastive Learning of Sentence Representations](https://mlsanity.com/paper/2109.05424/) (2021)
- [Virtual Augmentation Supported Contrastive Learning of Sentence Representations](https://mlsanity.com/paper/2110.08552/) (2021)
- [AnswerSumm: A Manually-Curated Dataset and Pipeline for Answer Summarization](https://mlsanity.com/paper/2111.06474/) (2021)
- [Robust Representation Learning with Reliable Pseudo-labels Generation via Self-Adaptive Optimal Transport for Short Text Clustering](https://mlsanity.com/paper/2305.16335/) (2023)
- [Short Text Clustering with Transformers](https://mlsanity.com/paper/2102.00541/) (2021)
- [Comprehensive Evaluation of Large Language Models for Topic Modeling](https://mlsanity.com/paper/2406.00697/) (2024)
- [DocSCAN: Unsupervised Text Classification via Learning from Neighbors](https://mlsanity.com/paper/2105.04024/) (2021)
- [No Pattern, No Recognition: a Survey about Reproducibility and Distortion Issues of Text Clustering and Topic Modeling](https://mlsanity.com/paper/2208.01712/) (2022)
- [TacoERE: Cluster-aware Compression for Event Relation Extraction](https://mlsanity.com/paper/2405.06890/) (2024)
- [Contrastive Learning Subspace for Text Clustering](https://mlsanity.com/paper/2408.14119/) (2024)
- [CEIL: A General Classification-Enhanced Iterative Learning Framework for Text Clustering](https://mlsanity.com/paper/2304.11061/) (2023)
- [Reliable Pseudo-labeling via Optimal Transport with Attention for Short Text Clustering](https://mlsanity.com/paper/2501.15194/) (2025)
- [Beyond Looking Up, Try Looking Around: Harmonizing Global Structure and Local Consistency in Optimal Transport for Short Text Clustering](https://mlsanity.com/paper/2607.10548/) (2026)

## Abstract (arXiv)

Short text clustering is a challenging task due to the lack of signal contained in such short texts. In this work, we propose iterative classification as a method to b o ost the clustering quality (e.g., accuracy) of short texts. Given a clustering of short texts obtained using an arbitrary clustering algorithm, iterative classification applies outlier removal to obtain outlier-free clusters. Then it trains a classification algorithm using the non-outliers based on their cluster distributions. Using the trained classification model, iterative classification reclassifies the outliers to obtain a new set of clusters. By repeating this several times, we obtain a much improved clustering of texts. Our experimental results show that the proposed clustering enhancement method not only improves the clustering quality of different clustering methods (e.g., k-means, k-means--, and hierarchical clustering) but also outperforms the state-of-the-art short text clustering methods on several short text datasets by a statistically significant margin.