PRISM: PRIor from corpus Statistics for topic Modeling

ACL ARR 2025 July Submission173 Authors

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 will be released upon acceptance.

Paper Type: Long

Research Area: Machine Learning for NLP

Research Area Keywords: Topic Modeling, Unsupervised Learning, Machine Learning for NLP, NLP Applications for Biology

Contribution Types: Model analysis & interpretability, NLP engineering experiment, Approaches to low-resource settings, Approaches low compute settings-efficiency

Languages Studied: English

Submission Number: 173