We give a uni ed account of the probabilistic semantics underlying the language modeling approach and the traditional probabilistic model for information retrieval, showing that the two approaches can be viewed as being equivalent probabilistically, since they are based on dierent factorizations of the same generative relevance model. We also discuss how the two approaches lead to dierent retrieval frameworks in practice, since they involve component models that are estimated quite dierently.