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        <title>Evaluating Dynamic Topic Models</title>
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        <namePart type="given">Charu</namePart>
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        <namePart type="family">Haji Ghassemi</namePart>
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            <namePart type="given">Vivek</namePart>
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    <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, including DTMs from large language models (LLMs). We also show that the proposed measure correlates well with human judgment. Our findings may help in identifying changing topics, evaluating different DTMs and LLMs, and guiding future research in this area.</abstract>
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%0 Conference Proceedings
%T Evaluating Dynamic Topic Models
%A Karakkaparambil James, Charu
%A Nagda, Mayank
%A Haji Ghassemi, Nooshin
%A Kloft, Marius
%A Fellenz, Sophie
%Y Ku, Lun-Wei
%Y Martins, Andre
%Y Srikumar, Vivek
%S Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand
%F karakkaparambil-james-etal-2024-evaluating
%X 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, including DTMs from large language models (LLMs). We also show that the proposed measure correlates well with human judgment. Our findings may help in identifying changing topics, evaluating different DTMs and LLMs, and guiding future research in this area.
%U https://aclanthology.org/2024.acl-long.11
%P 160-176

Markdown (Informal)

[Evaluating Dynamic Topic Models](https://aclanthology.org/2024.acl-long.11) (Karakkaparambil James et al., ACL 2024)

• Evaluating Dynamic Topic Models (Karakkaparambil James et al., ACL 2024)

ACL

• Charu Karakkaparambil James, Mayank Nagda, Nooshin Haji Ghassemi, Marius Kloft, and Sophie Fellenz. 2024. Evaluating Dynamic Topic Models. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 160–176, Bangkok, Thailand. Association for Computational Linguistics.