Evaluating topic models for information retrieval

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.