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@ -17314,6 +17314,38 @@ manipulate tree data.")
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R packages (on CRAN, Bioconductor or Github).")
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R packages (on CRAN, Bioconductor or Github).")
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(license license:artistic2.0)))
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(license license:artistic2.0)))
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(define-public r-doc2vec
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(package
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(name "r-doc2vec")
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(version "0.2.0")
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(source (origin
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(method url-fetch)
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(uri (cran-uri "doc2vec" version))
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(sha256
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(base32
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"0249hm0103kxxsi4gks4h20wf6p00gbrk9jf8c148mbja1l56f6v"))))
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(properties `((upstream-name . "doc2vec")))
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(build-system r-build-system)
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(propagated-inputs (list r-rcpp))
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(home-page "https://github.com/bnosac/doc2vec")
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(synopsis "Distributed representations of sentences, documents and topics")
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(description
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"Learn vector representations of sentences, paragraphs or documents by
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using the Paragraph Vector algorithms, namely the distributed bag of
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words (PV-DBOW) and the distributed memory (PV-DM) model. Top2vec finds
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clusters in text documents by combining techniques to embed documents and
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words and density-based clustering. It does this by embedding documents in
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the semantic space as defined by the doc2vec algorithm. Next it maps these
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document embeddings to a lower-dimensional space using the Uniform Manifold
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Approximation and Projection (UMAP) clustering algorithm and finds dense areas
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in that space using a Hierarchical Density-Based Clustering
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technique (HDBSCAN). These dense areas are the topic clusters which can be
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represented by the corresponding topic vector which is an aggregate of the
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document embeddings of the documents which are part of that topic cluster. In
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the same semantic space similar words can be found which are representative of
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the topic.")
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(license license:expat)))
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(define-public r-docopt
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(define-public r-docopt
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(package
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(package
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(name "r-docopt")
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(name "r-docopt")
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