CRAN/E | topicmodels.etm

topicmodels.etm

Topic Modelling in Embedding Spaces

Installation

About

Find topics in texts which are semantically embedded using techniques like word2vec or Glove. This topic modelling technique models each word with a categorical distribution whose natural parameter is the inner product between a word embedding and an embedding of its assigned topic. The techniques are explained in detail in the paper 'Topic Modeling in Embedding Spaces' by Adji B. Dieng, Francisco J. R. Ruiz, David M. Blei (2019), available at .

System requirements LibTorch (https://pytorch.org/)

Key Metrics

Version 0.1.0
R ≥ 2.10
Published 2021-11-08 899 days ago
Needs compilation? no
License MIT
License File
CRAN checks topicmodels.etm results

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Maintainer

Maintainer

Jan Wijffels

jwijffels@bnosac.be

Authors

Jan Wijffels

aut / cre / cph

(R implementation)

BNOSAC

cph

(R implementation)

Adji B. Dieng

ctb / cph

(original Python implementation in inst/orig)

Francisco J. R. Ruiz

ctb / cph

(original Python implementation in inst/orig)

David M. Blei

ctb / cph

(original Python implementation in inst/orig)

Material

README
NEWS
Reference manual
Package source

macOS

r-release

arm64

r-oldrel

arm64

r-release

x86_64

r-oldrel

x86_64

Windows

r-devel

x86_64

r-release

x86_64

r-oldrel

x86_64

Depends

R ≥ 2.10

Imports

graphics
stats
Matrix
torch ≥ 0.5.0

Suggests

udpipe ≥ 0.8.4
word2vec
uwot
tinytest
textplot ≥0.2.0
ggrepel
ggalt