doc2concrete: Measuring Concreteness in Natural Language

Models for detecting concreteness in natural language. This package is built in support of Yeomans (2021) <doi:10.1016/j.obhdp.2020.10.008>, which reviews linguistic models of concreteness in several domains. Here, we provide an implementation of the best-performing domain-general model (from Brysbaert et al., (2014) <doi:10.3758/s13428-013-0403-5>) as well as two pre-trained models for the feedback and plan-making domains.

Version: 0.5.4
Depends: R (≥ 3.5.0)
Imports: tm, quanteda, parallel, glmnet, stringr, english, textstem, SnowballC, stringi
Suggests: knitr, rmarkdown, testthat
Published: 2021-05-17
Author: Mike Yeomans
Maintainer: Mike Yeomans <mk.yeomans at gmail.com>
License: MIT + file LICENSE
NeedsCompilation: no
Materials: README
CRAN checks: doc2concrete results

Documentation:

Reference manual: doc2concrete.pdf
Vignettes: doc2concrete

Downloads:

Package source: doc2concrete_0.5.4.tar.gz
Windows binaries: r-devel: doc2concrete_0.5.4.zip, r-release: doc2concrete_0.5.4.zip, r-oldrel: doc2concrete_0.5.4.zip
macOS binaries: r-release (arm64): doc2concrete_0.5.4.tgz, r-release (x86_64): doc2concrete_0.5.4.tgz, r-oldrel: doc2concrete_0.5.4.tgz
Old sources: doc2concrete archive

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