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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 209.2 kB | noarch/r-lsa-0.73.3-r45ha770c72_4.conda  3 days and 8 hours ago 27 main
conda 212.5 kB | noarch/r-lsa-0.73.3-r44ha770c72_4.conda  3 days and 8 hours ago 40 main
conda 215.7 kB | noarch/r-lsa-0.73.3-r43ha770c72_3.conda  1 year and 2 months ago 1438 main
conda 213.0 kB | noarch/r-lsa-0.73.3-r44ha770c72_3.conda  1 year and 2 months ago 1241 main
conda 216.1 kB | noarch/r-lsa-0.73.3-r42ha770c72_2.conda  2 years and 3 months ago 1743 main
conda 215.5 kB | noarch/r-lsa-0.73.3-r43ha770c72_2.conda  2 years and 3 months ago 1866 main
conda 219.4 kB | noarch/r-lsa-0.73.3-r42ha770c72_1.tar.bz2  2 years and 11 months ago 2205 main
conda 219.4 kB | noarch/r-lsa-0.73.3-r41ha770c72_1.tar.bz2  2 years and 11 months ago 2079 main
conda 219.0 kB | noarch/r-lsa-0.73.3-r41ha770c72_0.tar.bz2  3 years and 4 months ago 2936 main
conda 219.1 kB | noarch/r-lsa-0.73.3-r40ha770c72_0.tar.bz2  3 years and 4 months ago 3579 main

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