R package 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.
Keywords for this software
References in zbMATH (referenced in 4 articles )
Showing results 1 to 4 of 4.
- Taylor B. Arnold: A Tidy Data Model for Natural Language Processing using cleanNLP (2017) arXiv
- Emily, Mathieu; Hitte, Christophe; Mom, Alain: SMILE: a novel dissimilarity-based procedure for detecting sparse-specific profiles in sparse contingency tables (2016)
- Michel Meulders: An R Package for Probabilistic Latent Feature Analysis of Two-Way Two-Mode Frequencies (2013) not zbMATH
- Ingo Feinerer; Kurt Hornik; David Meyer: Text Mining Infrastructure in R (2008) not zbMATH