lsa: Latent Semantic Analysis
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.
Documentation:
Downloads:
Reverse dependencies:
| Reverse depends: |
AurieLSHGaussian, LSAfun |
| Reverse imports: |
aPEAR, ccmap, CellScore, conversim, CoreGx, DTWBI, DTWUMI, GeneNMF, IBCF.MTME, OmicsQC, RESOLVE, WordListsAnalytics |
| Reverse suggests: |
quanteda, quanteda.textmodels, Signac, SpatialDDLS |
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