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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.
Version: | 0.73.3 |
Depends: | SnowballC |
Suggests: | tm |
Published: | 2022-05-09 |
DOI: | 10.32614/CRAN.package.lsa |
Author: | Fridolin Wild |
Maintainer: | Fridolin Wild <wild at brookes.ac.uk> |
License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
NeedsCompilation: | no |
Materials: | ChangeLog |
In views: | NaturalLanguageProcessing |
CRAN checks: | lsa results |
Reference manual: | lsa.pdf |
Package source: | lsa_0.73.3.tar.gz |
Windows binaries: | r-devel: lsa_0.73.3.zip, r-release: lsa_0.73.3.zip, r-oldrel: lsa_0.73.3.zip |
macOS binaries: | r-release (arm64): lsa_0.73.3.tgz, r-oldrel (arm64): lsa_0.73.3.tgz, r-release (x86_64): lsa_0.73.3.tgz, r-oldrel (x86_64): lsa_0.73.3.tgz |
Old sources: | lsa archive |
Reverse depends: | AurieLSHGaussian, LSAfun |
Reverse imports: | aPEAR, ccmap, CellScore, CoreGx, DTWBI, DTWUMI, GeneNMF, IBCF.MTME, OmicsQC, RESOLVE |
Reverse suggests: | quanteda, quanteda.textmodels, Signac, SpatialDDLS |
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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