Abstract
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.
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© 2003 Springer Science+Business Media Dordrecht
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Lafferty, J., Zhai, C. (2003). Probabilistic Relevance Models Based on Document and Query Generation. In: Croft, W.B., Lafferty, J. (eds) Language Modeling for Information Retrieval. The Springer International Series on Information Retrieval, vol 13. Springer, Dordrecht. https://doi.org/10.1007/978-94-017-0171-6_1
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DOI: https://doi.org/10.1007/978-94-017-0171-6_1
Publisher Name: Springer, Dordrecht
Print ISBN: 978-90-481-6263-5
Online ISBN: 978-94-017-0171-6
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