Treffer: Machine learning for knowledge-based document routing: a report on the TREC-2 experiment
Title:
Machine learning for knowledge-based document routing: a report on the TREC-2 experiment
Authors:
Source:
TREC-2: Text retrieval conferenceNIST special publication. (500215):253-264
Publisher Information:
Gaithersburg, MD: National Institute of Standards and Technology, 1994.
Publication Year:
1994
Physical Description:
print, 7 ref
Original Material:
INIST-CNRS
Subject Terms:
Science technology, industry, Sciences et technologies, industries, 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, Informatique documentaire, Documentation data processing, Información documental, Acheminement, Routing, Encaminamiento, Algorithme, Algorithm, Algoritmo, Apprentissage, Learning, Aprendizaje, Arbre décision, Decision tree, Arbol decisión, Classification automatique, Automatic classification, Clasificación automática, Conception système, System design, Concepción sistema, Essai, Test, Ensayo, Indice, Index, Jugement, Judgment, Juicio, Logiciel documentaire, Information retrieval software, Logicial documental, Méthode statistique, Statistical method, Método estadístico, Optimisation, Optimization, Optimización, Pertinence, Relevance, Pertinencia, Question documentaire, Query, Pregunta documental, Système documentaire, Document retrieval system, Sistema recuperación documental, Système expert, Expert system, Sistema experto, Système recherche, Search system, Sistema investigación, Texte intégral, Full text, Texto completo, CART (Classification and Regression Trees), Orienté question, Query oriented, Représentation question, Query processing, TOPIC, TREC-2
Document Type:
Konferenz
Conference Paper
File Description:
text
Language:
English
Author Affiliations:
Advanced Decision Systems, Mountain View CA 94043, United States
ISSN:
1048-776X
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.3464821
Database:
PASCAL Archive
Weitere Informationen
This paper contains a description of the experiments performed by Advanced Decision Systems as part of the Second Retrieval Conference (TREC-2). The overall system developed for TREC-2 demonstrates how it can be combined statistically techniques with a commercially available knowledge-based information retrieval system. As in TREC-1, the tool used for the fully automatic construction of routing queries is based on the Classification and Regression Trees (CART) algorithm. However, in, a departure from TREC-1, we have expanded our definition of what constitutes a document featureswithin the CART output can be used as the basis of topic definitions that can be interpreted by the TOPIC retrieval system developed by Veery, Inc.