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Treffer: Document indexing and retrieval using natural language processing

Title:
Document indexing and retrieval using natural language processing
Authors:
STRZALKOWSKI, T
Source:
Intelligent multimedia information retrieval systems and management (New York NY, October 11-12, 1994). :131-143
Publisher Information:
Paris: CID, 1994.
Publication Year:
1994
Physical Description:
print, 21 ref
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, Traitement et recherche de l'information, Information processing and retrieval, Structure et analyse des documents et de l'information, Information and document structure and analysis, Analyse des contenus, Content analysis, Indexation. Classification. Résumé. Synthèses, Indexing. Classification. Abstracting. Syntheses, Sciences de l'information et de la communication, Information and communication sciences, Traitement et recherche d'information, Informatique documentaire, Documentation data processing, Información documental, Linguistique mathématique, Computational linguistics, Linguística matemática, Analyse documentaire, Document analysis, Análisis documental, Analyse syntaxique, Syntactic analysis, Análisis sintáxico, Extraction information, Information extraction, Extractión información, Indexation automatique, Automatic indexing, Indización automática, Langage naturel, Natural language, Lenguaje natural, Mot relié, Connected word, Palabra ligada, Mot, Word, Palabra, Méthode statistique, Statistical method, Método estadístico, Pondération, Weighting, Ponderación, Prétraitement, Pretreatment, Pretratamiento, Représentation par terme indexation, Search pattern, Representación por término indexación, Syntagme, Syntagm, Sintagma, Traitement associé, Combined treatment, Tratamiento asociado, Traitement langage, Language processing, Tratamiento lenguaje, Expansion question, Query processing, Orienté mot, Word oriented, Traitement question
Document Type:
Konferenz Conference Paper
File Description:
text
Language:
English
Author Affiliations:
New York univ., courant inst. mathematical sci., New York NY 10003, United States
Access URL:
http://pascal-francis.inist.fr/vibad/index.php?action=search&terms=3553671
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.3553671
Database:
PASCAL Archive

Weitere Informationen

In information retrieval, the content of a document may be represented as a collection of terms: words, stems, phrases, or other units derived or inferred from the text of the document. These terms are usually weighted to indicate their importance within the document which can the be viewed as a vector in a N-dimensional espace. In this paper we demonstrate that a proper term weighting is at least as important as their selection and that different types of terms (e.g, words, phrases, names), and terms derived by different means (e. g. statistical, linguistic) must be treated differently for a maximum benefit in retrieval. We report results of selected experiments with our prototype natural language information retrieval performed in connection with the second Text REtrieval Conference (TREC-2) using a 550 MBytes Wall Street journal database and a and a 300 Mbytes San Jose Mercury database.