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doc2vec
Distributed Representations of Sentences, Documents and Topics
View on CRAN: Click
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Download and install doc2vec package within the R console
Install from CRAN:
install.packages("doc2vec")
Install from Github:
library("remotes")
install_github("cran/doc2vec") Install by package version:
library("remotes")
install_version("doc2vec", "0.2.0") Attach the package and use:
library("doc2vec")
Maintained by
Jan Wijffels
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
10.32614/CRAN.package.doc2vec . https://github.com/bnosac/doc2vec . doc2vec results . doc2vec.pdf . doc2vec_0.2.0.tar.gz . doc2vec_0.2.0.zip . doc2vec_0.2.0.zip . doc2vec_0.2.0.zip . doc2vec_0.2.0.tgz . doc2vec_0.2.0.tgz . doc2vec_0.2.0.tgz . doc2vec_0.2.0.tgz . doc2vec_0.2.0.tgz . doc2vec_0.2.0.tgz . doc2vec archive . https://CRAN.R-project.org/package=doc2vec .
First Published: 2020-12-10
Latest Update: 2021-03-28
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.
The techniques in the package are detailed in the paper "Distributed Representations of Sentences and Documents" by Mikolov et al. (2014), available at .
The package also provides an implementation to cluster documents based on these embedding using a technique called top2vec.
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.
More details can be found in the paper 'Top2Vec: Distributed Representations of Topics' by D. Angelov available at .
How to cite:
Jan Wijffels (2020). doc2vec: Distributed Representations of Sentences, Documents and Topics. R package version 0.2.0, https://cran.r-project.org/web/packages/doc2vec. Accessed 29 Mar. 2025.
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imports, suggests or enhances
Complete documentation for doc2vec
Functions, R codes and Examples using
the doc2vec R package
Some associated functions: as.matrix.paragraph2vec . be_parliament_2020 . paragraph2vec . paragraph2vec_similarity . predict.paragraph2vec . read.paragraph2vec . summary.top2vec . top2vec . txt_count_words . update.top2vec . write.paragraph2vec .
Some associated R codes: RcppExports.R . data.R . paragraph2vec.R . pkg.R . top2vec.R . utils.R . Full doc2vec package functions and examples
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