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fwild
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.
Maintained by Fridolin Wild. Last updated 3 years ago.
6.25 score 1.2k scripts 23 dependents
kplevoet
svs:Tools for Semantic Vector Spaces
Various tools for semantic vector spaces, such as correspondence analysis (simple, multiple and discriminant), latent semantic analysis, probabilistic latent semantic analysis, non-negative matrix factorization, latent class analysis, EM clustering, logratio analysis and log-multiplicative (association) analysis. Furthermore, there are specialized distance measures, plotting functions and some helper functions.
Maintained by Koen Plevoets. Last updated 9 months ago.
1 stars 1.49 score 31 scripts