We introduce and create a framework for deriving probabilistic models of Information Retrieval. The models are nonparametric models of IR obtained in the language model approach. We derive term-weighting models by measuring the divergence of the actual term distribution from that obtained under a random process. Among the random processes we study the binomial distribution and Bose-Einstein statistics. We define two types of term frequency normalization for tuning term weights in the document-query matching process. The first normalization assumes that documents have the same length and measures the information gain with the observed term once it has been accepted as a good descriptor of the observed document. The second normalization is related to the document length and to other statistics. These two normalization methods are applied to the basic models in succession to obtain weighting formulae. Results show that our framework produces different nonparametric models forming baseline alternatives to the standard tf-idf model.
REF proposed a nonparametric probabilistic model (called DFR) where term weights are computed by measuring the divergence between a term distribution produced by a random process and the actual term distribution.
Probabilistic models of information retrieval based on measuring the divergence from randomness
TOIS
Computer Science