Evaluating Dynamic Topic Models

September 12, 2023 ยท Declared Dead ยท ๐Ÿ› Annual Meeting of the Association for Computational Linguistics

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Authors Charu James, Mayank Nagda, Nooshin Haji Ghassemi, Marius Kloft, Sophie Fellenz arXiv ID 2309.08627 Category cs.CL: Computation & Language Cross-listed cs.IR, cs.LG Citations 5 Venue Annual Meeting of the Association for Computational Linguistics Last Checked 4 months ago
Abstract
There is a lack of quantitative measures to evaluate the progression of topics through time in dynamic topic models (DTMs). Filling this gap, we propose a novel evaluation measure for DTMs that analyzes the changes in the quality of each topic over time. Additionally, we propose an extension combining topic quality with the model's temporal consistency. We demonstrate the utility of the proposed measure by applying it to synthetic data and data from existing DTMs. We also conducted a human evaluation, which indicates that the proposed measure correlates well with human judgment. Our findings may help in identifying changing topics, evaluating different DTMs, and guiding future research in this area.
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