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.
|Date of publication||2015-05-08 19:58:09|
|Maintainer||Fridolin Wild <firstname.lastname@example.org>|
|License||GPL (>= 2)|
alnumx: Regular expression for removal of non-alphanumeric characters...
associate: Find close terms in a textmatrix
as.textmatrix: Display a latent semantic space generated by Latent Semantic...
corpora: Corpora (Essay Scoring)
cosine: Cosine Measure (Matrices)
dimcalc: Dimensionality Calculation Routines (LSA)
foldin: Ex-post folding-in of textmatrices into an existing latent...
lsa: Create a vector space with Latent Semantic Analysis (LSA)
print.textmatrix: Print a textmatrix (Matrices)
query: Query (Matrices)
sample.textmatrix: Create a random sample of files
specialchars: List of special character html entities and their character...
stopwords: Stopwordlists in German, English, Dutch, French, Polish, and...
summary.textmatrix: Summary of a textmatrix (Matrices)
textmatrix: Textmatrix (Matrices)
triples: Bind Triples to a Textmatrix
weightings: Weighting Schemes (Matrices)