# TopiCLEAR: Adaptive embedding clustering for interpretable topic discovery from short texts

arXiv: https://arxiv.org/abs/2512.06694 · HTML: https://mlsanity.com/paper/2512.06694/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 48 references that are in the map.
- First posted on arXiv 7 Dec 2025.

## Builds on (most cited first, 16 of 16)

- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Character-level Convolutional Networks for Text Classification](https://mlsanity.com/paper/1509.01626/) (2015)
- [MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformers](https://mlsanity.com/paper/2002.10957/) (2020)
- [Text Embeddings by Weakly-Supervised Contrastive Pre-training](https://mlsanity.com/paper/2212.03533/) (2022)
- [BERTopic: Neural topic modeling with a class-based TF-IDF procedure](https://mlsanity.com/paper/2203.05794/) (2022)
- [Topic Modeling in Embedding Spaces](https://mlsanity.com/paper/1907.04907/) (2019)
- [Autoencoding Variational Inference For Topic Models](https://mlsanity.com/paper/1703.01488/) (2017)
- [Is Automated Topic Model Evaluation Broken?: The Incoherence of Coherence](https://mlsanity.com/paper/2107.02173/) (2021)
- [Cross-lingual Contextualized Topic Models with Zero-shot Learning](https://mlsanity.com/paper/2004.07737/) (2020)
- [Topic Modelling Meets Deep Neural Networks: A Survey](https://mlsanity.com/paper/2103.00498/) (2021)
- [Short Text Topic Modeling Techniques, Applications, and Performance: A Survey](https://mlsanity.com/paper/1904.07695/) (2019)
- [Benchmarking Prompt Sensitivity in Large Language Models](https://mlsanity.com/paper/2502.06065/) (2025)
- [Twitter Topic Classification](https://mlsanity.com/paper/2209.09824/) (2022)
- [Public Perception of Generative AI on Twitter: An Empirical Study Based on Occupation and Usage](https://mlsanity.com/paper/2305.09537/) (2023)
- [Topic Modeling over Short Texts by Incorporating Word Embeddings](https://mlsanity.com/paper/1609.08496/) (2016)
- [Evolution of the public opinion on COVID-19 vaccination in Japan](https://mlsanity.com/paper/2207.10924/) (2022)

## Abstract (arXiv)

Topic discovery is a fundamental technique for text mining that identifies abstract topics within large document collections. A recent approach to topic discovery is to cluster document or sentence embeddings, typically obtained from pre-trained language models, and represent each cluster as a topic. Despite their strong empirical performance, the design principles linking the geometry of embedding spaces to human-interpretable topic organization remain unclear. Clarifying the relationship between these geometric structures and topic interpretability is therefore a key challenge in topic discovery. In this study, we propose TopiCLEAR (Topic discovery by CLustering Embeddings with Adaptive dimensionality Reduction), a simple framework that integrates document embeddings with iterative clustering based on adaptive dimensionality reduction. TopiCLEAR is guided by the hypothesis that human-interpretable topics correspond to low-dimensional geometric structures in embedding spaces and leverages adaptive dimensionality reduction to identify them. We evaluate topic quality using a document-level approach that combines quantitative evaluation based on human-labeled data with qualitative assessment in the absence of ground-truth labels. Experiments on four benchmark datasets, covering both formal and informal texts, show that TopiCLEAR consistently achieves strong agreement with human annotations, particularly for short and informal texts. Furthermore, a case study on Twitter data demonstrates that TopiCLEAR produces more interpretable topics than Latent Dirichlet Allocation (LDA), recovering both human-annotated topic structure and coherent sub-topic structure. These results highlight the effectiveness of clustering documents in a low-dimensional topic space for topic discovery.