Models for retrieval with probabilistic indexing
Information Processing and Management: an International Journal - Modeling data, information and knowledge
The probability ranking principle in IR
Readings in information retrieval
Inference networks for document retrieval
Readings in 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)
A probabilistic model of information retrieval: development and comparative experiments
Information Processing and Management: an International Journal
Information Retrieval
Modern Information Retrieval
Explicit relevance models in intent-oriented information retrieval diversification
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
Personalized diversification of search results
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
The likelihood property in general retrieval operations
Information Sciences: an International Journal
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This paper proposes a measure of relevance likelihood derived specifically for language models. Such a measure may be used to guide a user on how far to browse through the list of retrieved items or for pseudo-relevance feedback. To derive this measure, it is necessary to make the assumption that a user is seeking an ideal (usually non-existent) document and the actual relevant documents in the collection will contain fragments of this ideal document. Thus, in deriving this measure we propose a novel way of capturing relevance in Language Modelling.