The probability ranking principle in IR
Readings in information retrieval
Foundations of statistical natural language processing
Foundations of statistical natural language processing
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
Probabilistic models of information retrieval based on measuring the divergence from randomness
ACM Transactions on Information Systems (TOIS)
A study of smoothing methods for language models applied to information retrieval
ACM Transactions on Information Systems (TOIS)
Statistical Language Models for Information Retrieval A Critical Review
Foundations and Trends in Information Retrieval
Divergence measures based on the Shannon entropy
IEEE Transactions on Information Theory
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Over the years a number of models have been introduced as solutions to the central IR problem of ranking documents given textual queries. Here we define another new model. It is a probabilistic model and has no term inter-dependencies, thus allowing calculation from inverted indices. It is based upon a simple core hypothesis, directly calculating a ranking score in terms of probability theory. Early results show that its performance is credible, even in the absence of parameters or heuristics. Its semantic basis gives absolute results, allowing different rankings to be compared with each other. The investigation of this model is at a very early stage; here, we simply propose the model for further investigation.