Probabilistic Relevance Models Based on Document and Query Generation

Probabilistic Relevance Models Based on Document and Query Generation (2002)

by John Lafferty , Chengxiang Zhai

Venue:	Language Modeling and Information Retrieval
Citations:	55 - 11 self

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Datum	Value	Source
TITLE	Probabilistic Relevance Models Based on Document and Query Generation	user correction - Legacy Corrections
AUTHOR NAME	John Lafferty	user correction - Legacy Corrections
AUTHOR NAME	Chengxiang Zhai	user correction - Legacy Corrections
AUTHOR AFFIL	School of Computer Science School of Computer Science; Carnegie Mellon University Carnegie Mellon University	user correction - Legacy Corrections
AUTHOR ADDR	Pittsburgh, PA 15213 Pittsburgh, PA 15213	user correction - Legacy Corrections
ABSTRACT	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.	user correction - Legacy Corrections
YEAR	2002	user correction - Legacy Corrections
VENUE	Language Modeling and Information Retrieval	INFERENCE
VENUE TYPE	CONFERENCE	INFERENCE
PAGES	1--10	INFERENCE
CITATIONS	15 found	ParsCit 1.0