A simple non-parametric Topic Mixture for Authors and Documents

TL;DR

This paper introduces a non-parametric extension of the Author-Topic Model using Hierarchical Dirichlet Processes, allowing flexible modeling without predefining the number of topics, and provides an efficient Gibbs sampling method.

Contribution

It presents a novel non-parametric extension to the Author-Topic Model based on Hierarchical Dirichlet Processes, with a simple Gibbs sampler implementation.

Findings

Effective modeling without prior topic number assumptions

Minimal additional theoretical and implementation complexity

Potential for improved topic discovery in authorship analysis

Abstract

This article reviews the Author-Topic Model and presents a new non-parametric extension based on the Hierarchical Dirichlet Process. The extension is especially suitable when no prior information about the number of components necessary is available. A blocked Gibbs sampler is described and focus put on staying as close as possible to the original model with only the minimum of theoretical and implementation overhead necessary.