Fit the Latent Semantic Analysis scaling model to a dfm, which may be
weighted (for instance using
the dfm on which the model will be fit
the number of dimensions to be included in output
margin to be smoothed by the SVD
svds in the RSpectra package is applied to enable the fast computation of the SVD.
The number of dimensions
nd retained in LSA is an empirical
issue. While a reduction in k can remove much of the noise, keeping
too few dimensions or factors may lose important information.
Haiyan Wang and Kohei Watanabe
Rosario, B. (2000). Latent Semantic Indexing: An Overview. Technical report INFOSYS 240 Spring Paper, University of California, Berkeley.
Deerwester, S., Dumais, S.T., Furnas, G.W., Landauer, T.K., & Harshman, R. (1990). Indexing by Latent Semantic Analysis. Journal of the American Society for Information Science, 41(6): 391.
1 2 3 4 5 6 7 8 9 10 11 12
dfmat <- quanteda::dfm(data_corpus_irishbudget2010) # create an LSA space and return its truncated representation in the low-rank space tmod <- textmodel_lsa(dfmat[1:10, ]) head(tmod$docs) # matrix in low_rank LSA space tmod$matrix_low_rank[,1:5] # fold queries into the space generated by dfmat[1:10,] # and return its truncated versions of its representation in the new low-rank space pred <- predict(tmod, newdata = dfmat[11:14, ]) pred$docs_newspace
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.