Presents a unified mathematical definition for the classical models of information retrieval and identifies a mathematical structure behind relevance feedback. Highlights include vector information retrieval; probabilistic information retrieval; and similarity information retrieval. (Contains 118 references.) (Author/LRW)

Discussion of document ranking methods to calculate the conceptual distance between a Boolean query and a document focuses on the Knowledge-Based Extension Boolean Model which evaluates weighted queries and documents effectively and avoids problems of previous methods. Semantic networks are discussed, and is-a hierarchies are explained. (21…

Presents a relevance-feedback strategy that improves the effectiveness of information-retrieval systems based on back-propagation of the relevance of retrieved documents using an algorithm developed in a neural approach. Describes a neural information-retrieval model and reports results obtained with the algorithm in three different environments.…

Discussion of hypertext browsing proposes a filtering algorithm which restricts the amount of information made available to the user by calculating the set of most relevant hypertext nodes for the user, utilizing the user profile and data clustering technique. An example is provided of an optimal cluster of relevant data items. (Author/LRW)

Presents a preliminary empirical test of a maximum likelihood approach to using relevance data for training information retrieval parameters. Discusses similarities to language models; the unification of document-oriented and query-oriented views; tests on data sets; algorithms and scalability; and the effectiveness of maximum likelihood…

Discussion of connectionist views for adaptive clustering in information retrieval focuses on a connectionist clustering technique and activation spreading-based information retrieval model using the interaction information retrieval method. Presents theoretical as well as simulation results as regards computational complexity and includes…

Describes an information retrieval methodology based on relevance weighting of search terms in pairs of documents. The vector space model of information retrieval is reviewed, the Bayesian model of text retrieval is discussed, and an automatic implementation of weight estimation using the Medline database is described. (16 references) (LRW)

Discussion of relevance feedback in information retrieval focuses on a proposal for the Bayesian Network Retrieval Model. Bases the proposal on the propagation of partial evidences in the Bayesian network, representing new information obtained from the user's relevance judgments to compute the posterior relevance probabilities of the documents…

Discusses term weighting formulae and their use for selecting new terms to enhance a search statement and for weighting the terms for retrieval purposes once selected. The Swets model of information retrieval system performance is described, an approach to term selection is presented, and future research is suggested. (five references) (LRW)

Explains how a competition-based connectionist model for diagnostic problem-solving is adapted to information retrieval. Topics include probabilistic causal networks; Bayesian networks; the neural network model; empirical studies of test collections that evaluated retrieval performance; precision results; and the use of a thesaurus to provide…

Discussion of information retrieval and relevance focuses on mutual information, a measure which represents the relation between two words. A model of a natural-language information-retrieval system that is based on a two-level document-ranking method using mutual information is presented, and a Korean encyclopedia test collection is explained.…

Discussion of user queries in information retrieval highlights the experimental evaluation of an adaptive linear model that constructs improved query vectors from user preference judgments on a sample set of documents. The performance of this method is compared with that of standard relevance feedback techniques. (28 references) (LRW)

Discusses precision and recall in information science and proposes a new model based on fractal geometry for clusters of relevant documents. Search strategies for retrieving a group of relevant documents are reviewed; fractal sets and chaotic processes are described; and the new model is explained. (Contains 43 references.) (LRW)

Discussion of the use of Latent Semantic Indexing to determine relevancy in information retrieval focuses on statistical regression and Bayesian methods. Topics include keyword searching; a multiple regression model; how the regression model can aid search methods; and limitations of this approach, including complexity, linearity, 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)