Topic Intrusion for Automatic Topic Model Evaluation

Abstract

Topic coherence is increasingly being used to evaluate topic models and filter topics for enduser applications. Topic coherence measures how well topic words relate to each other, but offers little insight into the utility of the topics in describing the documents. In this paper, we explore the topic intrusion task — the task of guessing an outlier topic given a document and a set of topics — and propose a method to automate it. We improve upon the state-of-the-art substantially, demonstrating its viability as an alternative method for topic model evaluation.