Discusses concepts and theories of information and suggests a model for assessment of information-as-contents. Proposes a revised formulation of Brookes' fundamental equation and possible approaches for describing attributes of the beneficiaries and their knowledge structure. (JKP)

Presents a multilevel model of the information filtering process that permits document classification. Evaluates a document classification approach based on a supervised learning algorithm, measures the accuracy of the algorithm in a neural network that was trained to classify medical documents on cell biology, and discusses filtering…

Describes a study that computed low performance standards for the group of queries in 13 information retrieval (IR) test collections. Derived from the random graph hypothesis, these standards represent the highest levels of retrieval effectiveness that can be obtained from meaningless clustering structures. (Author/LRW)

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…

Analyzes relevance assessments of topical descriptors for bibliographic records for two dimensions: (1) a vertical conceptual hierarchy of broad to narrow descriptors, and (2) a horizontal linkage of related terms. The data were analyzed for a semantic distance and semantic direction effect as postulated by the Semantic Distance Model. (Author/LRW)

Describes a study that computed the low performance standards for queries in 17 test collections. Predicted by the hypergeometric distribution, the standards represent the highest level of retrieval effectiveness attributable to chance. Operational levels of performance for vector-space and other retrieval models were compared to the standards.…

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…

Reviews the TREC (Text Retrieval Conference) program, considering the test results, the findings for information retrieval, and the lessons TREC offers for information retrieval evaluation. Topics include the ad hoc retrieval task; indexing models; document and query descriptions; search strategies; and the user's request as the dominant factor in…

Introduces an evaluative framework for metasearch engine performance and illustrates its use in two experiments. Results are used to characterize some properties of leading search engines (as of 1998). Significant interactions were observed between search engine and two other factors: time of day and Web domain. (Author/AEF)

Describes and evaluates a learning model for information filtering and selective dissemination of information which is an adaptation of the generalized probabilistic model of information retrieval. The model is based on the concept of uncertainty sampling that allows for relevance feedback both on relevant and nonrelevant documents. (Author/LRW)