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: | 3.0.2 |
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: | 2020-03-08 |
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 (>= 3.7.0) |
Citation: | cleanNLP citation info |
Materials: | NEWS |
CRAN checks: | cleanNLP results |
Reference manual: | cleanNLP.pdf |
Vignettes: |
Exploring the State of the Union Addresses: A Case Study with cleanNLP Creating Text Visualizations with Wikipedia Data |
Package source: | cleanNLP_3.0.2.tar.gz |
Windows binaries: | r-devel: cleanNLP_3.0.2.zip, r-release: cleanNLP_3.0.2.zip, r-oldrel: cleanNLP_3.0.2.zip |
macOS binaries: | r-release: cleanNLP_3.0.2.tgz, r-oldrel: cleanNLP_3.0.2.tgz |
Old sources: | cleanNLP archive |
Reverse enhances: | NLP |
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