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: | 3.0.3 |
| 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: | 2020-10-13 |
| Author: | Taylor B. Arnold [aut, cre] |
| Maintainer: | Taylor B. Arnold <taylor.arnold at acm.org> |
| 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 |
| 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_3.0.3.tar.gz |
| Windows binaries: | r-devel: cleanNLP_3.0.3.zip, r-release: cleanNLP_3.0.3.zip, r-oldrel: cleanNLP_3.0.3.zip |
| macOS binaries: | r-release (arm64): cleanNLP_3.0.3.tgz, r-oldrel (arm64): cleanNLP_3.0.3.tgz, r-release (x86_64): cleanNLP_3.0.3.tgz, r-oldrel (x86_64): cleanNLP_3.0.3.tgz |
| Old sources: | cleanNLP archive |
| Reverse enhances: | NLP |
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