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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, conversim, CoreGx, DTWBI, DTWUMI, GeneNMF, IBCF.MTME, OmicsQC, OutSeekR, RESOLVE, WordListsAnalytics |
| Reverse suggests: | quanteda, quanteda.textmodels, Signac, SpatialDDLS |
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These binaries (installable software) and packages are in development.
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