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The basic idea of latent semantic analysis (LSA) is, that text do have a higher order (=latent semantic) structure which, however, is obscured by word usage (e.g. through the use of synonyms or polysemy). By using conceptual indices that are derived statistically via a truncated singular value decomposition (a two-mode factor analysis) over a given document-term matrix, this variability problem can be overcome.

copied from cf-post-staging / r-lsa
Type Size Name Uploaded Downloads Labels
conda 218.4 kB | noarch/r-lsa-0.73.2-r41ha770c72_1.tar.bz2  4 years and 4 months ago 3097 main
conda 218.5 kB | noarch/r-lsa-0.73.2-r40ha770c72_1.tar.bz2  4 years and 4 months ago 2975 main
conda 218.1 kB | noarch/r-lsa-0.73.2-r40_1.tar.bz2  5 years and 4 months ago 3683 main
conda 218.3 kB | noarch/r-lsa-0.73.2-r36_1.tar.bz2  5 years and 4 months ago 3449 main
conda 217.4 kB | noarch/r-lsa-0.73.2-r35_0.tar.bz2  5 years and 4 months ago 3233 main
conda 218.3 kB | noarch/r-lsa-0.73.2-r36_0.tar.bz2  5 years and 4 months ago 3314 main

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