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.
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Reverse dependencies:
| Reverse depends: | AurieLSHGaussian,LSAfun |
| Reverse imports: | conversim,CoreGx,DTWBI,DTWUMI,GeneNMF,IBCF.MTME,MD2sample,OmicsQC,OutSeekR,RESOLVE,SemanticDistance,WordListsAnalytics |
| Reverse suggests: | quanteda,quanteda.textmodels,Signac |
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