Treffer: Query expansion and neural network
Title:
Query expansion and neural network
Authors:
Source:
Intelligent multimedia information retrieval systems and management (New York NY, October 11-12, 1994). :519-532
Publisher Information:
Paris: CID, 1994.
Publication Year:
1994
Physical Description:
print, 2 p
Original Material:
INIST-CNRS
Subject Terms:
Documentation, 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, Systèmes de recherche d'informations. Système de gestion documentaire et d'information, Information retrieval systems. Information and document management system, Systèmes de recherche d'information, Information retrieval systems, Sciences de l'information et de la communication, Information and communication sciences, Système de recherche documentaire. Système de gestion documentaire et d'information, Connexionnisme, Connectionism, Conexionismo, Informatique documentaire, Documentation data processing, Información documental, Activation, Activación, Adaptation, Adaptación, Apprentissage, Learning, Aprendizaje, Intégration, Integration, Integración, Propagation, Propagación, Prototype, Prototipo, Représentation par terme indexation, Search pattern, Representación por término indexación, Réseau neuronal, Neural network, Red neuronal, Système adaptatif, Adaptive system, Sistema adaptativo, Système classique, Classical system, Sistema clásico, Système recherche, Search system, Sistema investigación, Analyse cooccurrence, Cooccurrence analysis, Expansion question, Query expansion, MERCURE, Modèle connexionniste, Connectionnist model, RECOLTE
Document Type:
Konferenz
Conference Paper
File Description:
text
Language:
English
Author Affiliations:
Univ. sci. Limoges, MSI, dep. informatique, 87060 Limoges, France
Univ. Paul Sabatier, IRIT/SIG, 31062 Toulouse, France
Univ. Paul Sabatier, IRIT/SIG, 31062 Toulouse, France
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
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
FRANCIS
Accession Number:
edscal.3553817
Database:
PASCAL Archive
Weitere Informationen
Automatic query expansion methods has historically been an important method for increasing the performance of Information Retreival Systems (IRS). In this paper, we present an IRS based on three-layer neural network. This network is able to adapt itself to the user's needs by restructuring the information in the base, on the basis of the user's reaction to the retrieved documents. In other words, the user's judgments of the relevance or of the non relevance documents are used to modify the weighted links connecting different term nodes in the network. The preliminary results indicate the usefulness of neural netrworks in adaptative information retrieval system.