cleanNLP: A Tidy Data Model for Natural Language Processing

Provides a set of fast tools for converting a textual corpus into a set of normalized tables. Users may make use of the 'udpipe' back end with no external dependencies, or two Python back ends with 'spaCy' <https://spacy.io> or 'CoreNLP' <http://stanfordnlp.github.io/CoreNLP/>. Exposed annotation tasks include tokenization, part of speech tagging, named entity recognition, and dependency parsing.

Version: 3.0.0
Depends: R (≥ 3.5.0)
Imports: Matrix (≥ 1.2), udpipe, reticulate, stringi, stats, methods
Suggests: knitr (≥ 1.15), rmarkdown (≥ 1.4), testthat (≥ 1.0.1), covr (≥ 2.2.2)
Published: 2019-10-22
Author: Taylor B. Arnold [aut, cre]
Maintainer: Taylor B. Arnold <taylor.arnold at acm.org>
BugReports: http://github.com/statsmaths/cleanNLP/issues
License: LGPL-2
URL: https://statsmaths.github.io/cleanNLP/
NeedsCompilation: no
SystemRequirements: Python (>= 3.6.0)
Citation: cleanNLP citation info
Materials: NEWS
CRAN checks: cleanNLP results

Downloads:

Reference manual: cleanNLP.pdf
Package source: cleanNLP_3.0.0.tar.gz
Windows binaries: r-devel: cleanNLP_2.3.0.zip, r-release: cleanNLP_2.3.0.zip, r-oldrel: cleanNLP_2.3.0.zip
OS X binaries: r-release: cleanNLP_2.3.0.tgz, r-oldrel: cleanNLP_2.3.0.tgz
Old sources: cleanNLP archive

Reverse dependencies:

Reverse enhances: NLP

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