LatentSemanticAnalysisWorkflows

Latent Semantic Analysis Workflows

This Raku package directory is obsolete.

The package was moved to: https://github.com/antononcube/Raku-DSL-English-LatentSemanticAnalysisWorkflows .

In brief

This Raku Perl 6 package has grammar classes and action classes for the parsing and interpretation of spoken commands that specify Latent Semantic Analysis (LSA) workflows.

It is envisioned that the interpreters (actions) are going to target different programming languages: R, Mathematica, Python, etc.

The generated pipelines are for the software monads LSAMon-R and LSAMon-WL, [AA1, AA2].

Installation

1. Install Raku (Perl 6) : https://raku.org/downloads .

2. Install Zef Module Installer : https://github.com/ugexe/zef .

3. Open a command line program. (E.g. Terminal on Mac OS X.)

4. Download or clone this repository, ConversationalAgents at GitHub. E.g.

git clone https://github.com/antononcube/ConversationalAgents.git

5. Go to the directory "ConversationalAgents/Packages/Perl6/LatentSemanticAnalysisWorkflows". E.g.

cd ConversationalAgents/Packages/Perl6/LatentSemanticAnalysisWorkflows

6. Execute the command:

zef install . --force-install --force-test

Examples

Open a Raku IDE or type raku in the command line program. Try this Raku code:

use LatentSemanticAnalysisWorkflows;

say to_LSAMon_R("extract 12 topics using method NNMF and max steps 12");
# LSAMonExtractTopics( numberOfTopics = 12, method = "NNMF",  maxSteps = 12)

Here is a more complicated pipeline specification:

say to_LSAMon_R(
  "create from textHamlet;
   make document term matrix with stemming FALSE and automatic stop words;
   apply LSI functions global weight function IDF, local term weight function TermFrequency, normalizer function Cosine;
   extract 12 topics using method NNMF and max steps 12 and 20 min number of documents per term;
   show topics table with 12 terms;
   show thesaurus table for king, castle, denmark;")

The command above should print out R code for the R package LSAMon-R, [AA1]:

LSAMonUnit(textHamlet) %>%
LSAMonMakeDocumentTermMatrix( stemWordsQ = FALSE, stopWords = NULL) %>%
LSAMonApplyTermWeightFunctions(globalWeightFunction = "IDF", localWeightFunction = "None", normalizerFunction = "Cosine") %>%
LSAMonExtractTopics( numberOfTopics = 12, method = "NNMF",  maxSteps = 12, minNumberOfDocumentsPerTerm = 20) %>%
LSAMonEchoTopicsTable(numberOfTerms = 12) %>%
LSAMonEchoStatisticalThesaurus( words = c("king", "castle", "denmark"))

References

[AA1] Anton Antonov, Latent Semantic Analysis Monad in R, (2019), R-packages at GitHub.

[AA2] Anton Antonov, Monadic Latent Semantic Analysis Mathematica package, (2017), MathematicaForPrediction at GitHub.