Treffer: Full text retrieval based on probabilistic equations with coefficients fitted by logistic regression
Title:
Full text retrieval based on probabilistic equations with coefficients fitted by logistic regression
Authors:
Source:
TREC-2: Text retrieval conferenceNIST special publication. (500215):57-66
Publisher Information:
Gaithersburg, MD: National Institute of Standards and Technology, 1994.
Publication Year:
1994
Physical Description:
print, 9 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, Apprentissage, Learning, Aprendizaje, Etalonnage, Calibration, Contraste, Etude expérimentale, Experimental study, Estudio experimental, Formule mathématique, Mathematical formula, Fórmula matemática, Jugement, Judgment, Juicio, Modèle probabiliste, Probabilistic model, Modelo probabilista, Méthode calcul, Computing method, Método cálculo, Prototype, Prototipo, Prédiction, Prediction, Predicción, Question documentaire, Query, Pregunta documental, Recherche documentaire, Document retrieval, Recuperación documental, Régression logistique, Logistic regression, Regresión logística, Système documentaire, Document retrieval system, Sistema recuperación documental, Texte intégral, Full text, Texto completo, Collection test, Test collection, Rangement, Ranking, TREC-2
Document Type:
Konferenz
Conference Paper
File Description:
text
Language:
English
Author Affiliations:
Univ. California, SLIS, Berkeley CA 94720, 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.3461415
Database:
PASCAL Archive
Weitere Informationen
The experiments described here ara part of research program whose objective is to develop a full text retrieval methodology that is statistically sound and powerful, yet reasonably simple. The methodology is based on the use of a probabilistic model whose parameters ara fitted empirically to a learning set of relevance judgements by logistic regression. The method was applied to the TIPSTER data with optimally relativized frequencies of occurence of match stems as the regression variables. In a routing retrieval experiment, these were supplemented by other variables coresponding to sums of logodds associated with particular match stems.