Probabilistic Relevance Models Based on Document and Query Generation
We give a unified 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 different factorizations of the same generative relevance model. We also discuss how the two approaches lead to different retrieval frameworks in practice, since they involve component models that are estimated quite differently.