# Discriminative Topic Mining via Category-Name Guided Text Embedding

arXiv: https://arxiv.org/abs/1908.07162 · HTML: https://mlsanity.com/paper/1908.07162/

## Key facts

- Cited by 29 papers in the map (data through 7 Oct 2026).
- First posted on arXiv 20 Aug 2019.

## Built on by (most cited first, 29 of 29)

- [Goal-Driven Explainable Clustering via Language Descriptions](https://mlsanity.com/paper/2305.13749/) (2023)
- [Effective Neural Topic Modeling with Embedding Clustering Regularization](https://mlsanity.com/paper/2306.04217/) (2023)
- [Topic Discovery via Latent Space Clustering of Pretrained Language Model Representations](https://mlsanity.com/paper/2202.04582/) (2022)
- [TaxoCom: Topic Taxonomy Completion with Hierarchical Discovery of Novel Topic Clusters](https://mlsanity.com/paper/2201.06771/) (2022)
- [ReGen: Zero-Shot Text Classification via Training Data Generation with Progressive Dense Retrieval](https://mlsanity.com/paper/2305.10703/) (2023)
- [vONTSS: vMF based semi-supervised neural topic modeling with optimal transport](https://mlsanity.com/paper/2307.01226/) (2023)
- [Neighborhood-Regularized Self-Training for Learning with Few Labels](https://mlsanity.com/paper/2301.03726/) (2023)
- [BERTrend: Neural Topic Modeling for Emerging Trends Detection](https://mlsanity.com/paper/2411.05930/) (2024)
- [Effective Seed-Guided Topic Discovery by Integrating Multiple Types of Contexts](https://mlsanity.com/paper/2212.06002/) (2022)
- [Beyond Prompting: Making Pre-trained Language Models Better Zero-shot Learners by Clustering Representations](https://mlsanity.com/paper/2210.16637/) (2022)
- [MotifClass: Weakly Supervised Text Classification with Higher-order Metadata Information](https://mlsanity.com/paper/2111.04022/) (2021)
- [Keyword Assisted Embedded Topic Model](https://mlsanity.com/paper/2112.03101/) (2021)
- [A Survey on Retrieval And Structuring Augmented Generation with Large Language Models](https://mlsanity.com/paper/2509.10697/) (2025)
- [Seed-Guided Topic Discovery with Out-of-Vocabulary Seeds](https://mlsanity.com/paper/2205.01845/) (2022)
- [LIME: Weakly-Supervised Text Classification Without Seeds](https://mlsanity.com/paper/2210.06720/) (2022)
- [WOT-Class: Weakly Supervised Open-world Text Classification](https://mlsanity.com/paper/2305.12401/) (2023)
- [A Benchmark on Extremely Weakly Supervised Text Classification: Reconcile Seed Matching and Prompting Approaches](https://mlsanity.com/paper/2305.12749/) (2023)
- [MEGClass: Extremely Weakly Supervised Text Classification via Mutually-Enhancing Text Granularities](https://mlsanity.com/paper/2304.01969/) (2023)
- [Coordinated Topic Modeling](https://mlsanity.com/paper/2210.08559/) (2022)
- [S2vNTM: Semi-supervised vMF Neural Topic Modeling](https://mlsanity.com/paper/2307.04804/) (2023)
- [TopicAdapt- An Inter-Corpora Topics Adaptation Approach](https://mlsanity.com/paper/2310.04978/) (2023)
- [Interactive Topic Models with Optimal Transport](https://mlsanity.com/paper/2406.19928/) (2024)
- [Synergizing Unsupervised Episode Detection with LLMs for Large-Scale News Events](https://mlsanity.com/paper/2408.04873/) (2024)
- [Enhancing Topic Interpretability for Neural Topic Modeling through Topic-wise Contrastive Learning](https://mlsanity.com/paper/2412.17338/) (2024)
- [Empirical Study of Named Entity Recognition Performance Using Distribution-aware Word Embedding](https://mlsanity.com/paper/2109.01636/) (2021)
- [BERT-Flow-VAE: A Weakly-supervised Model for Multi-Label Text Classification](https://mlsanity.com/paper/2210.15225/) (2022)
- [LITA: An Efficient LLM-assisted Iterative Topic Augmentation Framework](https://mlsanity.com/paper/2412.12459/) (2024)
- [Seeded Poisson Factorization: leveraging domain knowledge to fit topic models](https://mlsanity.com/paper/2503.02741/) (2025)
- [Clusters are All You Need: Pre-Training the Tsetlin Machine with Semantic Clusters from Language Models for Interpretability](https://mlsanity.com/paper/2606.19815/) (2026)

## Abstract (arXiv)

Mining a set of meaningful and distinctive topics automatically from massive text corpora has broad applications. Existing topic models, however, typically work in a purely unsupervised way, which often generate topics that do not fit users' particular needs and yield suboptimal performance on downstream tasks. We propose a new task, discriminative topic mining, which leverages a set of user-provided category names to mine discriminative topics from text corpora. This new task not only helps a user understand clearly and distinctively the topics he/she is most interested in, but also benefits directly keyword-driven classification tasks. We develop CatE, a novel category-name guided text embedding method for discriminative topic mining, which effectively leverages minimal user guidance to learn a discriminative embedding space and discover category representative terms in an iterative manner. We conduct a comprehensive set of experiments to show that CatE mines high-quality set of topics guided by category names only, and benefits a variety of downstream applications including weakly-supervised classification and lexical entailment direction identification.