# Self-Taught Convolutional Neural Networks for Short Text Clustering

arXiv: https://arxiv.org/abs/1701.00185 · HTML: https://mlsanity.com/paper/1701.00185/

## Key facts

- Cited by 34 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Jan 2017.

## Built on by (most cited first, 30 of 34)

- [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)
- [Recent Advances of Foundation Language Models-based Continual Learning: A Survey](https://mlsanity.com/paper/2405.18653/) (2024)
- [UCTopic: Unsupervised Contrastive Learning for Phrase Representations and Topic Mining](https://mlsanity.com/paper/2202.13469/) (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)
- [Towards Textual Out-of-Domain Detection without In-Domain Labels](https://mlsanity.com/paper/2203.11396/) (2022)
- [Beyond Prompting: Making Pre-trained Language Models Better Zero-shot Learners by Clustering Representations](https://mlsanity.com/paper/2210.16637/) (2022)
- [AnswerSumm: A Manually-Curated Dataset and Pipeline for Answer Summarization](https://mlsanity.com/paper/2111.06474/) (2021)
- [UGSL: A Unified Framework for Benchmarking Graph Structure Learning](https://mlsanity.com/paper/2308.10737/) (2023)
- [Simple-Sampling and Hard-Mixup with Prototypes to Rebalance Contrastive Learning for Text Classification](https://mlsanity.com/paper/2405.11524/) (2024)
- [The Power of Summary-Source Alignments](https://mlsanity.com/paper/2406.00842/) (2024)
- [Extracting Sentence Embeddings from Pretrained Transformer Models](https://mlsanity.com/paper/2408.08073/) (2024)
- [Contrastive Learning with Prompt-derived Virtual Semantic Prototypes for Unsupervised Sentence Embedding](https://mlsanity.com/paper/2211.03348/) (2022)
- [Robust Representation Learning with Reliable Pseudo-labels Generation via Self-Adaptive Optimal Transport for Short Text Clustering](https://mlsanity.com/paper/2305.16335/) (2023)
- [A Query-Driven Topic Model](https://mlsanity.com/paper/2106.07346/) (2021)
- [Clustering-Aware Negative Sampling for Unsupervised Sentence Representation](https://mlsanity.com/paper/2305.09892/) (2023)
- [Multi-Perspective Abstractive Answer Summarization](https://mlsanity.com/paper/2104.08536/) (2021)
- [A User-Centered, Interactive, Human-in-the-Loop Topic Modelling System](https://mlsanity.com/paper/2304.01774/) (2023)
- [Multi-turn Dialogue Comprehension from a Topic-aware Perspective](https://mlsanity.com/paper/2309.09666/) (2023)
- [A new approach for fine-tuning sentence transformers for intent classification and out-of-scope detection tasks](https://mlsanity.com/paper/2410.13649/) (2024)
- [GloCOM: A Short Text Neural Topic Model via Global Clustering Context](https://mlsanity.com/paper/2412.00525/) (2024)
- [Short Text Clustering with Transformers](https://mlsanity.com/paper/2102.00541/) (2021)
- [CLIP also Understands Text: Prompting CLIP for Phrase Understanding](https://mlsanity.com/paper/2210.05836/) (2022)
- [Information-Theoretic Generative Clustering of Documents](https://mlsanity.com/paper/2412.13534/) (2024)
- [DALR: Dual-level Alignment Learning for Multimodal Sentence Representation Learning](https://mlsanity.com/paper/2506.21096/) (2025)
- [SPOT: Knowledge-Enhanced Language Representations for Information Extraction](https://mlsanity.com/paper/2208.09625/) (2022)
- [Contrastive Learning Subspace for Text Clustering](https://mlsanity.com/paper/2408.14119/) (2024)
- [Deep Clustering with Measure Propagation](https://mlsanity.com/paper/2104.08967/) (2021)
- [CEIL: A General Classification-Enhanced Iterative Learning Framework for Text Clustering](https://mlsanity.com/paper/2304.11061/) (2023)

## Abstract (arXiv)

Short text clustering is a challenging problem due to its sparseness of text representation. Here we propose a flexible Self-Taught Convolutional neural network framework for Short Text Clustering (dubbed STC^2), which can flexibly and successfully incorporate more useful semantic features and learn non-biased deep text representation in an unsupervised manner. In our framework, the original raw text features are firstly embedded into compact binary codes by using one existing unsupervised dimensionality reduction methods. Then, word embeddings are explored and fed into convolutional neural networks to learn deep feature representations, meanwhile the output units are used to fit the pre-trained binary codes in the training process. Finally, we get the optimal clusters by employing K-means to cluster the learned representations. Extensive experimental results demonstrate that the proposed framework is effective, flexible and outperform several popular clustering methods when tested on three public short text datasets.