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' <https://stanfordnlp.github.io/CoreNLP/>. Exposed annotation tasks include tokenization, part of speech tagging, named entity recognition, and dependency parsing.

Version:	3.0.7
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:	2023-11-16
Author:	Taylor B. Arnold [aut, cre]
Maintainer:	Taylor B. Arnold <tarnold2 at richmond.edu>
BugReports:	https://github.com/statsmaths/cleanNLP/issues
License:	LGPL-2
URL:	https://statsmaths.github.io/cleanNLP/
NeedsCompilation:	no
SystemRequirements:	Python (>= 3.7.0)
Citation:	cleanNLP citation info
Materials:	NEWS
CRAN checks:	cleanNLP results

Documentation:

Reference manual:	cleanNLP.pdf
Vignettes:	Exploring the State of the Union Addresses: A Case Study with cleanNLP Creating Text Visualizations with Wikipedia Data

Downloads:

Package source:	cleanNLP_3.0.7.tar.gz
Windows binaries:	r-devel: cleanNLP_3.0.7.zip, r-release: cleanNLP_3.0.7.zip, r-oldrel: cleanNLP_3.0.7.zip
macOS binaries:	r-release (arm64): cleanNLP_3.0.7.tgz, r-oldrel (arm64): cleanNLP_3.0.7.tgz, r-release (x86_64): cleanNLP_3.0.7.tgz, r-oldrel (x86_64): cleanNLP_3.0.7.tgz
Old sources:	cleanNLP archive

Reverse dependencies:

Reverse enhances:

NLP

Linking:

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