We validate an eye-tracking method applicable for studying memory processes in complex cognitive tasks. The method is tested with a task on probabilistic inferences from memory. It provides valuable data on the time course of processing, thus clarifying previous results on heuristic probabilistic inference. Participants learned cue values of…

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…

Assesses the retrieval effectiveness of automatically constructed interdocument hypertext links in information retrieval (IR). Describes experiments using statistical and probabilistic techniques that were designed to obtain evidence concerning the usefulness of querying and browsing automatically constructed IR hypertexts. Results indicate a…

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)

Discusses information retrieval systems and the need to have them adapt to user needs, integrate information in any format, reveal patterns and trends in information, and answer questions. Topics include statistics and probability; natural language processing; intelligent agents; concept mapping; machine-aided indexing; text mining; filtering;…

Discusses methods of weighting of descriptors in document search patterns together with the concept of relative indexing and conditions which should be satisfied by descriptor weight. A procedure for modifiying document search patterns based on users' informational needs and opinions is outlined, and examples are given. Sixteen references are…

Examines the use of various statistical techniques for generating domain-oriented multiword vocabulary terms for natural language database systems. Conclusions show the vocabulary clustering effect should be considered when making significance calculations and that a simple ratio of subject matter relative frequency to total sample relative…