Copyright 1996 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.2987040
Database:
PASCAL Archive
Weitere Informationen
The idea of conceptual mapping goes back to the semantic differential and conceptual clustering. Using multivariate statistical techniques, one can map a dispersion of texts onto another dispersion of their content indicators, such as keywords. The resulting configurations of texts/indicators differ from one another according to their meaning, expressed in terms of co-ordinates of a semantic field. We suggest that by using principal component analysis, one can design a user-friendly semantic space which can be navigated. Further, to learn the names of embedded magnitudes in semantic space, the idea of conceptual clustering is used in a broader context. This is a two-mode statistical approach, grouping both documents and their index terms at the same time. By observing the agglomerations of narrower, related terms over a corpus, one arrives at broader, more general thesaurus entries which denote and conceptualise the major dimensions of semantic space.
