Examines statistical techniques for exploiting relevance information to weight search terms. These techniques are presented as a natural extension of weighting methods using information about the distribution of index terms in documents in general. (Author)

Suggests a probabilistic method to compute the term relationships from relevance information, which complements the studies on a nonprobabilistic technique called pseudo-classification. A quadratic ranking function is derived by incorporating the term-by-term relationships. Procedures for estimating the required parameters are provided by…

Introduces a new model of viewing documents based on citing-cited relationship between them. Using Bayes' decision theory, it is shown how source document may be indexed and weighted by relevant cited document features, corresponding to one pass relevance feedback Model 1 (probabilistic indexing) or Model 2 (probabilistic retrieval). (24…

Presents adaptation of a probabilistic theoretical model previously used in relevance feedback for use in automatic indexing of documents (in the sense of imitating) human indexers. Methods for model application are proposed, independence assumptions used in the model are interpreted, and the probability of a dependence model is discussed.…

Presents a theoretical rationale for employing a form of information retrieval algorithm that helps searchers better navigate through large document collections by continually revising probability estimates for document subsets. The algorithm avoids assumptions of index term independence and predicts relevance more accurately with increasing…

Presents results of simulations that were conducted to test the effects of errors in estimation of individual term probabilities on the performance of a probabilistic information retrieval (PIR) system. Assigning index terms to documents is described; an information retrieval system called Helpnet is explained; and relevance, ranking, and…

Describes the Belief Function Model for automatic indexing and ranking of documents which is based on a controlled vocabulary and on term frequencies in each document. Belief Function Theory is explained, and the Belief Function Model is compared to the Standard Vector Space Model. (17 references) (LRW)