Models for detecting concreteness in natural language. This package is built in support of Yeomans (2020) <doi:10.17605/OSF.IO/DYZN6>, 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: | 0.4.6 |
| Depends: | R (≥ 2.10) |
| Imports: | tm, quanteda, ggplot2, parallel, glmnet, stringr, dplyr, english, textstem, SnowballC, textclean |
| Suggests: | knitr, rmarkdown, testthat |
| Published: | 2020-06-19 |
| Author: | Mike Yeomans |
| Maintainer: | Mike Yeomans <mk.yeomans at gmail.com> |
| License: | MIT + file LICENSE |
| NeedsCompilation: | no |
| Materials: | README |
| CRAN checks: | doc2concrete results |
| Reference manual: | doc2concrete.pdf |
| Vignettes: |
doc2concrete |
| Package source: | doc2concrete_0.4.6.tar.gz |
| Windows binaries: | r-devel: doc2concrete_0.4.6.zip, r-release: doc2concrete_0.4.6.zip, r-oldrel: doc2concrete_0.4.6.zip |
| macOS binaries: | r-release: doc2concrete_0.4.6.tgz, r-oldrel: doc2concrete_0.4.6.tgz |
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