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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-staging / r-lsa
Type Size Name Uploaded Downloads Labels
conda 218.4 kB | noarch/r-lsa-0.73.1-r36_1001.tar.bz2  5 years and 7 months ago 3913 main cf202003
conda 217.1 kB | noarch/r-lsa-0.73.1-r35_1001.tar.bz2  5 years and 7 months ago 3813 main cf202003
conda 216.7 kB | noarch/r-lsa-0.73.1-r351_1000.tar.bz2  6 years and 3 months ago 4466 main cf202003 gcc7 cf201901
conda 204.8 kB | win-64/r-lsa-0.73.1-r3.3.2_0.tar.bz2  7 years and 27 days ago 1848 main cf202003 cf201901
conda 206.2 kB | win-64/r-lsa-0.73.1-r3.4.1_0.tar.bz2  7 years and 27 days ago 1830 main cf202003 cf201901
conda 192.4 kB | osx-64/r-lsa-0.73.1-r3.4.1_0.tar.bz2  7 years and 27 days ago 372 main cf202003 cf201901
conda 190.6 kB | osx-64/r-lsa-0.73.1-r3.3.2_0.tar.bz2  7 years and 27 days ago 370 main cf202003 cf201901
conda 192.0 kB | linux-64/r-lsa-0.73.1-r3.4.1_0.tar.bz2  7 years and 27 days ago 4999 main cf202003 cf201901
conda 190.3 kB | linux-64/r-lsa-0.73.1-r3.3.2_0.tar.bz2  7 years and 27 days ago 4935 main cf202003 cf201901

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