SIGIR '92 Proceedings of the 15th annual international ACM SIGIR conference on Research and development in information retrieval
Some simple effective approximations to the 2-Poisson model for probabilistic weighted retrieval
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
Large test collection experiments on an operational, interactive system: Okapi at TREC
TREC-2 Proceedings of the second conference on Text retrieval conference
Pivoted document length normalization
SIGIR '96 Proceedings of the 19th annual international ACM SIGIR conference on Research and development 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
The Importance of Prior Probabilities for Entry Page Search
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Probabilistic models of information retrieval based on measuring the divergence from randomness
ACM Transactions on Information Systems (TOIS)
Term Frequency Normalization via Pareto Distributions
Proceedings of the 24th BCS-IRSG European Colloquium on IR Research: Advances in Information Retrieval
Language Modeling for Information Retrieval
Language Modeling for Information Retrieval
The Geometry of Information Retrieval
The Geometry of Information Retrieval
On Event Spaces and Probabilistic Models in Information Retrieval
Information Retrieval
Relevance information: a loss of entropy but a gain for IDF?
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
A general matrix framework for modelling information retrieval
Information Processing and Management: an International Journal - Special issue: Formal methods for information retrieval
A study of Poisson query generation model for information retrieval
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Interpreting TF-IDF term weights as making relevance decisions
ACM Transactions on Information Systems (TOIS)
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 Generative Theory of Relevance
Journal of the American Society for Information Science and Technology
Query-based inter-document similarity using probabilistic co-relevance model
ECIR'08 Proceedings of the IR research, 30th European conference on Advances in information retrieval
Towards a better understanding of the relationship between probabilistic models in IR
ICTIR'11 Proceedings of the Third international conference on Advances in information retrieval theory
IR models: foundations and relationships
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
Back to the roots: a probabilistic framework for query-performance prediction
Proceedings of the 21st ACM international conference on Information and knowledge management
Probabilistic co-relevance for query-sensitive similarity measurement in information retrieval
Information Processing and Management: an International Journal
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
Hi-index | 0.00 |
This paper investigates in a stringent athematical formalism the parallel derivation of three grand probabilistic retrieval models: binary independent retrieval (BIR), Poisson model (PM), and language modelling (LM).The investigation has been motivated by a number of questions. Firstly, though sharing the same origin, namely the probability of relevance, the models differ with respect to event spaces. How can this be captured in a consistent notation, and can we relate the event spaces? Secondly, BIR and PM are closely related, but how does LM fit in? Thirdly, how are tf-idf and probabilistic models related? .The parallel investigation of the models leads to a number of formalised results: BIR and PM assume the collection to be a set of non-relevant documents, whereas LM assumes the collection to be a set of terms from relevant documents.PM can be viewed as a bridge connecting BIR and LM.A BIR-LM equivalence explains BIR as a special LM case.PM explains tf-idf, and both, BIR and LM probabilities express tf-idf in a dual way..