Search (7 results, page 1 of 1)

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Abstract

Results from research in information retrieval have suggested that significant improvements in retrieval effectiveness can be obtained by combining results from multiple index representioms, query formulations, and search strategies. The inference net model of retrieval, which was designed from this point of view, treats information retrieval as an evidental reasoning process where multiple sources of evidence about document and query content are combined to estimate relevance probabilities. Uses a system based on this model to study the retrieval effectiveness benefits of combining these types of document and query information that are found in typical commercial databases and information services. The results indicate that substantial real benefits are possible

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Abstract

Inquery is a text retrieval system that is the basis of a number of WWW applications, including the Thomas system supported by the Library of Congress. Surveys the representation, query processing, and retrieval techniques used in the system. By combining evidence about relevance from the corpus, individual documents, and users, Inquery achieves effective overall recall and precision evaluation while avoiding occasional major failures

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Abstract

Reports on an analysis of lexical ambiguity in information retrieval text collections and on experiments to determine the utility of word meanings for separating relevant from nonrelevant documents. Results show that there is considerable ambiguity even in a specialised database. Word senses provide a significant separation between relevant and nonrelevant documents, but several factors contribute to determining whether disambiguation will make an improvement in performance such as: resolving lexical ambiguity was found to have little impact on retrieval effectiveness for documents that have many words in common with the query. Discusses other uses of word sense disambiguation in an information retrieval context

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Abstract

Although commonly used in both commercial and experimental information retrieval systems, thesauri have not demonstrated consistent benefits for retrieval performance, and it is difficult to construct a thesaurus automatically for large text databases. In this paper, an approach, called PhraseFinder, is proposed to construct collection-dependent association thesauri automatically using large full-text document collections. The association thesaurus can be accessed through natural language queries in INQUERY, an information retrieval system based on the probabilistic inference network. Experiments are conducted in INQUERY to evaluate different types of association thesauri, and thesauri constructed for a variety of collections

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Source

Information processing and management. 29(1993) no.3, S.313-324

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Source

Communications of the Association for Computing Machinery. 35(1992) no.12, S.29-38

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Date

27. 2.1999 20:55:22