Transforming Selected Concepts Into Dimensions in Latent Semantic Analysis

This study presents a new approach for transforming the latent representation derived from a Latent Semantic Analysis (LSA) space into one where dimensions have nonlatent meanings. These meanings are based on lexical descriptors, which are selected by the LSA user. The authors present three analyses...

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Bibliographic Details
Published in:Discourse processes 2014-01, Vol.51 (5-6), p.494-510
Main Authors: Olmos, Ricardo, Jorge-Botana, Guillermo, León, José Antonio, Escudero, Inmaculada
Format: Article
Language:English
Subjects:
Classification
Methods
Natural Language Processing
Reliability
Semantics
Validity
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Summary:This study presents a new approach for transforming the latent representation derived from a Latent Semantic Analysis (LSA) space into one where dimensions have nonlatent meanings. These meanings are based on lexical descriptors, which are selected by the LSA user. The authors present three analyses that provide examples of the utility of this methodology. The first analysis demonstrates how document terms can be projected into meaningful new dimensions. The second demonstrates how to use the modified space to perform multidimensional document labeling to obtain a high and substantive reliability between LSA experts. Finally, the internal validity of the method is assessed by comparing an original semantic space with a modified space. The results show high consistency between the two spaces, supporting the conclusion that the nonlatent coordinates generated using this methodology preserve the semantic relationships within the original LSA space.
ISSN:0163-853X
1532-6950
DOI:10.1080/0163853X.2014.913416