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authorRicardo Wurmus <rekado@elephly.net>2023-06-13 14:38:00 +0200
committerRicardo Wurmus <rekado@elephly.net>2023-06-14 14:09:34 +0200
commit49711f621b4e42ffa45aafcd8829eb51cc135cce (patch)
tree560a60eb1477de8c11d9a74e9514fdeefda37285 /gnu/packages/cran.scm
parent6b34433c9054da3686f10360992e0bf85236e048 (diff)
gnu: Add r-doc2vec.
* gnu/packages/cran.scm (r-doc2vec): New variable.
Diffstat (limited to 'gnu/packages/cran.scm')
-rw-r--r--gnu/packages/cran.scm32
1 files changed, 32 insertions, 0 deletions
diff --git a/gnu/packages/cran.scm b/gnu/packages/cran.scm
index 33cc882030..89af513c8b 100644
--- a/gnu/packages/cran.scm
+++ b/gnu/packages/cran.scm
@@ -17314,6 +17314,38 @@ manipulate tree data.")
R packages (on CRAN, Bioconductor or Github).")
(license license:artistic2.0)))
+(define-public r-doc2vec
+ (package
+ (name "r-doc2vec")
+ (version "0.2.0")
+ (source (origin
+ (method url-fetch)
+ (uri (cran-uri "doc2vec" version))
+ (sha256
+ (base32
+ "0249hm0103kxxsi4gks4h20wf6p00gbrk9jf8c148mbja1l56f6v"))))
+ (properties `((upstream-name . "doc2vec")))
+ (build-system r-build-system)
+ (propagated-inputs (list r-rcpp))
+ (home-page "https://github.com/bnosac/doc2vec")
+ (synopsis "Distributed representations of sentences, documents and topics")
+ (description
+ "Learn vector representations of sentences, paragraphs or documents by
+using the Paragraph Vector algorithms, namely the distributed bag of
+words (PV-DBOW) and the distributed memory (PV-DM) model. Top2vec finds
+clusters in text documents by combining techniques to embed documents and
+words and density-based clustering. It does this by embedding documents in
+the semantic space as defined by the doc2vec algorithm. Next it maps these
+document embeddings to a lower-dimensional space using the Uniform Manifold
+Approximation and Projection (UMAP) clustering algorithm and finds dense areas
+in that space using a Hierarchical Density-Based Clustering
+technique (HDBSCAN). These dense areas are the topic clusters which can be
+represented by the corresponding topic vector which is an aggregate of the
+document embeddings of the documents which are part of that topic cluster. In
+the same semantic space similar words can be found which are representative of
+the topic.")
+ (license license:expat)))
+
(define-public r-docopt
(package
(name "r-docopt")