Evaluating Dynamic Topic Models
Charu Karakkaparambil James, Mayank Nagda, Nooshin Haji Ghassemi, Marius Kloft, Sophie Fellenz
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.
BibTeX
@inproceedings{karakkaparambil-james-etal-2024-evaluating,
title = "Evaluating Dynamic Topic Models",
author = "Karakkaparambil James, Charu and
Nagda, Mayank and
Haji Ghassemi, Nooshin and
Kloft, Marius and
Fellenz, Sophie",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.acl-long.11/",
doi = "10.18653/v1/2024.acl-long.11",
pages = "160--176"
}