When Numbers Tell Half the Story: Human-Metric Alignment in Topic Model Evaluation
Topic models uncover latent thematic structures in text corpora, yet evaluating their quality remains challenging, particularly in specialized domains. Existing methods often rely on automated metrics like topic coherence and diversity, which may not fully align with human judgment. Human evaluation tasks, such as word intrusion, provide valuable insights but are costly and primarily validated on general-domain corpora. This paper introduces Topic Word Mixing (TWM), a novel human evaluation task assessing inter-topic distinctness by testing whether annotators can distinguish between word sets from single or mixed topics. TWM complements word intrusion's focus on intra-topic coherence and ...
| Authors | Thibault Prouteau, Francis Lareau, Nicolas Dugué et al. |
|---|---|
| Primary category | cs.CL |
| Published | 2 March 2026 |
| Last updated at source | 2 March 2026 |
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