Large-scale correlation analysis of automated metrics for topic models

• Main Authors: LIM, Jia Peng, LAUW, Hady Wirawan
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2023
• Subjects: Automated metric; Coherence metric; Correlation analysis; Human decision-making; Human judgments; Large corpora; Large-scale correlations; Metric evaluation; Topic Modeling; User study; Databases and Information Systems
• Online Access: https://ink.library.smu.edu.sg/sis_research/8346; https://ink.library.smu.edu.sg/context/sis_research/article/9349/viewcontent/acl23.pdf
• Institution: Singapore Management University

Automated coherence metrics constitute an important and popular way to evaluate topic models. Previous works present a mixed picture of their presumed correlation with human judgement. In this paper, we conduct a large-scale correlation analysis of coherence metrics. We propose a novel sampling approach to mine topics for the purpose of metric evaluation, and conduct the analysis via three large corpora showing that certain automated coherence metrics are correlated. Moreover, we extend the analysis to measure topical differences between corpora. Lastly, we examine the reliability of human judgement by conducting an extensive user study, which is designed as an amalgamation of different proxy tasks to derive a finer insight into the human decision-making processes. Our findings reveal some correlation between automated coherence metrics and human judgement, especially for generic corpora.