Latent Semantic Analysis
Display a latent semantic space generated by Latent Semantic Analysis ...
Find close terms in a textmatrix
Corpora (Essay Scoring)
Cosine Measure (Matrices)
Dimensionality Calculation Routines (LSA)
Ex-post folding-in of textmatrices into an existing latent semantic sp...
Create a vector space with Latent Semantic Analysis (LSA)
Print a textmatrix (Matrices)
Query (Matrices)
Create a random sample of files
Stopwordlists in German, English, Dutch, French, Polish, and Arab
Summary of a textmatrix (Matrices)
Textmatrix (Matrices)
Bind Triples to a Textmatrix
Weighting Schemes (Matrices)
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.