PRISM: PRIor from corpus Statistics for topic Modeling

Tal Ishon, Yoav Goldberg, Uri Shaham

Abstract:

Topic modeling seeks to uncover latent semantic structure in text, with LDA providing a foundational probabilistic framework. While recent methods often incorporate external knowledge (e.g., pre-trained embeddings), such reliance limits applicability in emerging or underexplored domains. We introduce PRISM, a corpus-intrinsic method that derives a Dirichlet parameter from word co-occurrence statistics to initialize LDA without altering its generative process. Experiments on text and single cell RNA-seq data show that PRISM improves topic coherence and interpretability, rivaling models that rely on external knowledge. These results underscore the value of corpus-driven initialization for topic modeling in resource-constrained settings.

Code is available at: https://github.com/shaham-lab/PRISM.

Certifications: Featured Certification

Submission Type: Regular submission (no more than 12 pages of main content)

Assigned Action Editor: ~Kejun_Huang1

Submission Number: 6686