Topic Intrusion for Automatic Topic Model Evaluation

EMNLP 2018  ·  Shraey Bhatia, Jey Han Lau, Timothy Baldwin ·

Topic coherence is increasingly being used to evaluate topic models and filter topics for end-user applications. Topic coherence measures how well topic words relate to each other, but offers little insight on 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 few 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.

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