Probabilistic models in information retrieval
The Computer Journal - Special issue on information retrieval
A language modeling approach to information retrieval
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
“Is this document relevant?…probably”: a survey of probabilistic models in information retrieval
ACM Computing Surveys (CSUR)
Foundations of statistical natural language processing
Foundations of statistical natural language processing
A study of smoothing methods for language models applied to information retrieval
ACM Transactions on Information Systems (TOIS)
On Event Spaces and Probabilistic Models in Information Retrieval
Information Retrieval
A parallel derivation of probabilistic information retrieval models
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Probabilistic models for combining diverse knowledge sources in multimedia retrieval
Probabilistic models for combining diverse knowledge sources in multimedia retrieval
TF-IDF uncovered: a study of theories and probabilities
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
On event space and rank equivalence between probabilistic retrieval models
Information Retrieval
Statistical Language Models for Information Retrieval A Critical Review
Foundations and Trends in Information Retrieval
A risk minimization framework for information retrieval
Information Processing and Management: an International Journal - Special issue: Formal methods for information retrieval
IR models: foundations and relationships
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
IR Models: Foundations and Relationships
Proceedings of the 2013 Conference on the Theory of Information Retrieval
Probabilistic models in IR and their relationships
Information Retrieval
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Probability of relevance (PR) models are generally assumed to implement the Probability Ranking Principle (PRP) of IR, and recent publications claim that PR models and language models are similar. However, a careful analysis reveals two gaps in the chain of reasoning behind this statement. First, the PRP considers the relevance of particular documents, whereas PR models consider the relevance of any query-document pair. Second, unlike PR models, language models consider draws of terms and documents. We bridge the first gap by showing how the probability measure of PR models can be used to define the probabilistic model of the PRP. Furthermore, we argue that given the differences between PR models and language models, the second gap cannot be bridged at the probabilistic model level. We instead define a new PR model based on logistic regression, which has a similar score function to the one of the query likelihood model. The performance of both models is strongly correlated, hence providing a bridge for the second gap at the functional and ranking level. Understanding language models in relation with logistic regression models opens ample new research directions which we propose as future work.