CorEx Topic Model
Correlation Explanation (CorEx) provides a flexible framework for learning topics that are maximally informative about a corpus of text. The CorEx topic model makes few assumptions about the latent structure of the data, and flexibly incorporates domain knowledge through user-specified “anchor words.” Through anchor words, you can seed and guide the topic model towards topics of substantive interest, allowing you to interact with and refine topics in a way that is not possible with traditional topic models.
The details of the CorEx topic model and comparisons with unsupervised and semi-supervised variants of LDA are described in our TACL paper:
Gallagher, R. J., Reing, K., Kale, D., & Ver Steeg, G. (2017). Anchored Correlation Explanation: Topic Modeling with Minimal Domain Knowledge. Transactions of the Association for Computational Linguistics (TACL), 5, 529-542.
An open-source implementation of the CorEx topic model is available in Python on PyPi (
corextopic) and on Github.