# Anchored Correlation Explanation: Topic Modeling with Minimal Domain Knowledge

arXiv: https://arxiv.org/abs/1611.10277 · HTML: https://mlsanity.com/paper/1611.10277/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Nov 2016.

## Built on by (most cited first, 23 of 23)

- [A Survey of Race, Racism, and Anti-Racism in NLP](https://mlsanity.com/paper/2106.11410/) (2021)
- [Large Language Models Offer an Alternative to the Traditional Approach of Topic Modelling](https://mlsanity.com/paper/2403.16248/) (2024)
- [Topic Discovery via Latent Space Clustering of Pretrained Language Model Representations](https://mlsanity.com/paper/2202.04582/) (2022)
- [vONTSS: vMF based semi-supervised neural topic modeling with optimal transport](https://mlsanity.com/paper/2307.01226/) (2023)
- [HyperMiner: Topic Taxonomy Mining with Hyperbolic Embedding](https://mlsanity.com/paper/2210.10625/) (2022)
- [Effective Seed-Guided Topic Discovery by Integrating Multiple Types of Contexts](https://mlsanity.com/paper/2212.06002/) (2022)
- [Seed-Guided Topic Discovery with Out-of-Vocabulary Seeds](https://mlsanity.com/paper/2205.01845/) (2022)
- [A Unified Neural Network Model for Readability Assessment with Feature Projection and Length-Balanced Loss](https://mlsanity.com/paper/2210.10305/) (2022)
- [Coordinated Topic Modeling](https://mlsanity.com/paper/2210.08559/) (2022)
- [Exploring the Power of Topic Modeling Techniques in Analyzing Customer Reviews: A Comparative Analysis](https://mlsanity.com/paper/2308.11520/) (2023)
- [Retrieving Floods without Floodlights: Topic Models as Binary Classifiers for Extreme Climate Events in German News](https://mlsanity.com/paper/2605.03450/) (2026)
- [Unsupervised Keyphrase Extraction via Interpretable Neural Networks](https://mlsanity.com/paper/2203.07640/) (2022)
- [Seeded Hierarchical Clustering for Expert-Crafted Taxonomies](https://mlsanity.com/paper/2205.11602/) (2022)
- [S2vNTM: Semi-supervised vMF Neural Topic Modeling](https://mlsanity.com/paper/2307.04804/) (2023)
- [Weakly Supervised Text Classification on Free Text Comments in Patient-Reported Outcome Measures](https://mlsanity.com/paper/2308.06199/) (2023)
- [The Ontoverse: Democratising Access to Knowledge Graph-based Data Through a Cartographic Interface](https://mlsanity.com/paper/2408.03339/) (2024)
- [Tell, Don't Show: Leveraging Language Models' Abstractive Retellings to Model Literary Themes](https://mlsanity.com/paper/2505.23166/) (2025)
- [Beyond Accuracy: Interpreting Topic Representation in Suicide Ideation Detection Models](https://mlsanity.com/paper/2606.07714/) (2026)
- [BERT-Flow-VAE: A Weakly-supervised Model for Multi-Label Text Classification](https://mlsanity.com/paper/2210.15225/) (2022)
- [The Anatomy of Conspirators: Unveiling Traits using a Comprehensive Twitter Dataset](https://mlsanity.com/paper/2308.15154/) (2023)
- [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)
- [Constrained Non-negative Matrix Factorization for Guided Topic Modeling of Minority Topics](https://mlsanity.com/paper/2505.16493/) (2025)

## Abstract (arXiv)

While generative models such as Latent Dirichlet Allocation (LDA) have proven fruitful in topic modeling, they often require detailed assumptions and careful specification of hyperparameters. Such model complexity issues only compound when trying to generalize generative models to incorporate human input. We introduce Correlation Explanation (CorEx), an alternative approach to topic modeling that does not assume an underlying generative model, and instead learns maximally informative topics through an information-theoretic framework. This framework naturally generalizes to hierarchical and semi-supervised extensions with no additional modeling assumptions. In particular, word-level domain knowledge can be flexibly incorporated within CorEx through anchor words, allowing topic separability and representation to be promoted with minimal human intervention. Across a variety of datasets, metrics, and experiments, we demonstrate that CorEx produces topics that are comparable in quality to those produced by unsupervised and semi-supervised variants of LDA.