Anchored Correlation Explanation: Topic Modeling with Minimal Domain Knowledge

Understand

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.