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Treffer: Combining automatic and manual index representations in probabilistic retrieval

Title:
Combining automatic and manual index representations in probabilistic retrieval
Authors:
RAJASHEKAR, T. B, CROFT, W. B
Source:
Journal of the American Society for Information Science. 46(4):272-283
Publisher Information:
New York, NY: John Wiley & Sons, 1995.
Publication Year:
1995
Physical Description:
print, 30 ref
Original Material:
INIST-CNRS
Subject Terms:
Documentation, Computer science, Informatique, Sciences exactes et technologie, Exact sciences and technology, Sciences et techniques communes, Sciences and techniques of general use, Sciences de l'information. Documentation, Information science. Documentation, Traitement et recherche de l'information, Information processing and retrieval, Recherche de l'information. Relation homme machine, Information retrieval. Man machine relationship, Aspects cognitifs, Cognitive aspects, Sciences de l'information et de la communication, Information and communication sciences, Traitement et recherche d'information, Informatique documentaire, Documentation data processing, Información documental, Besoin utilisateur, User need, Necesidad usuario, Conception système, System design, Concepción sistema, Indexation automatique, Automatic indexing, Indización automática, Inférence, Inference, Inferencia, Jugement, Judgment, Juicio, Langage naturel, Natural language, Lenguaje natural, Meilleure approximation, Best approximation, Mejor aproximación, Modèle probabiliste, Probabilistic model, Modelo probabilista, Modèle théorique, Theoretical model, Modelo teórico, Multiple, Múltiple, Recherche documentaire, Document retrieval, Recuperación documental, Représentation par terme indexation, Search pattern, Representación por término indexación, Réseau, Network, Red, Système combiné, Combined system, Sistema combinado, Système conversationnel, Interactive system, Sistema conversacional, Système recherche, Search system, Sistema investigación, Test hypothèse, Hypothesis test, Test hipótesis, Tâche appariement, Matching task, Tarea apareamiento, INQUERY, Indexation humaine, Human indexing, Inference net model, Représentation document, Document representation, Représentation multiple, Multiple representation, Représentation question, Query representation
Document Type:
Fachzeitschrift Article
File Description:
text
Language:
English
Author Affiliations:
Indian inst. sci., national cent. sci. information, Bangalore 560 012, India
Univ. Massachusetts, computer sci. dep., Amherst MA 01003, United States
ISSN:
0002-8231
Access URL:
http://pascal-francis.inist.fr/vibad/index.php?action=search&terms=3498277
Rights:
Copyright 1995 INIST-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Notes:
Sciences of information and communication. Documentation

FRANCIS
Accession Number:
edscal.3498277
Database:
PASCAL Archive

Weitere Informationen

Results from research in information retrieval have suggested that significant improvements in retrieval effectiveness can be obtained by combining results from multiple index representations, query formulations, and search strategies. The inference net model of retrieval, which was designed from this point of view, treats information retrieval as an evidential reasoning process where multiple sources of evidence about document and query content are combined to estimate relevance probabilities. In this article, we use a system based on this model to study the retrieval effectiveness benefits of combining the 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.