Text Retrieval Priors for Bayesian Logistic Regression

Abstract

Discriminative learning algorithms such as logistic regression excel when training data are plentiful, but falter when it is meager. An extreme case is text retrieval (zero training data), where discriminative learning is impossible and heuristics such as BM25, which combine domain knowledge (a topical keyword query) with generative learning (Naive Bayes), are dominant. Building on past work, we show that BM25-inspired Gaussian priors for Bayesian logistic regression based on topical keywords provide better effectiveness than the usual L2 (zero mode, uniform variance) Gaussian prior. On two high recall retrieval datasets, the resulting models transition smoothly from BM25 level effectiveness to discriminative effectiveness as training data volume increases, dominating L2 regularization even when substantial training data is available.