Evaluating topic models for information retrieval

Experiments to measure topic models' ability to predict held-out likelihood confirm past results on small corpora, but suggest that simple approaches to topic model are better for large corpora.

Abstract

We explore the utility of different types of topic models, both probabilistic and not, for retrieval purposes. We show that: (1) topic models are effective for document smoothing; (2) more elaborate topic models that capture topic dependencies provide no additional gains; (3) smoothing documents by using their similar documents is as effective as smoothing them by using topic models; (4) topics discovered on the whole corpus are too coarse-grained to be useful for query expansion. Experiments to measure topic models' ability to predict held-out likelihood confirm past results on small corpora, but suggest that simple approaches to topic model are better for large corpora.