tokenizers: Fast, Consistent Tokenization of Natural Language Text

Convert natural language text into tokens. Includes tokenizers for shingled n-grams, skip n-grams, words, word stems, sentences, paragraphs, characters, shingled characters, lines, Penn Treebank, regular expressions, as well as functions for counting characters, words, and sentences, and a function for splitting longer texts into separate documents, each with the same number of words. The tokenizers have a consistent interface, and the package is built on the 'stringi' and 'Rcpp' packages for fast yet correct tokenization in 'UTF-8'.

Version: 0.3.0
Depends: R (≥ 3.1.3)
Imports: stringi (≥ 1.0.1), Rcpp (≥ 0.12.3), SnowballC (≥ 0.5.1)
LinkingTo: Rcpp
Suggests: covr, knitr, rmarkdown, stopwords (≥ 0.9.0), testthat
Published: 2022-12-22
DOI: 10.32614/CRAN.package.tokenizers
Author: Lincoln Mullen ORCID iD [aut, cre], Os Keyes ORCID iD [ctb], Dmitriy Selivanov [ctb], Jeffrey Arnold ORCID iD [ctb], Kenneth Benoit ORCID iD [ctb]
Maintainer: Lincoln Mullen <lincoln at lincolnmullen.com>
BugReports: https://github.com/ropensci/tokenizers/issues
License: MIT + file LICENSE
URL: https://docs.ropensci.org/tokenizers/, https://github.com/ropensci/tokenizers
NeedsCompilation: yes
Citation: tokenizers citation info
Materials: README NEWS
In views: NaturalLanguageProcessing
CRAN checks: tokenizers results

Documentation:

Reference manual: tokenizers.pdf
Vignettes: Introduction to the tokenizers Package
The Text Interchange Formats and the tokenizers Package

Downloads:

Package source: tokenizers_0.3.0.tar.gz
Windows binaries: r-devel: tokenizers_0.3.0.zip, r-release: tokenizers_0.3.0.zip, r-oldrel: tokenizers_0.3.0.zip
macOS binaries: r-release (arm64): tokenizers_0.3.0.tgz, r-oldrel (arm64): tokenizers_0.3.0.tgz, r-release (x86_64): tokenizers_0.3.0.tgz, r-oldrel (x86_64): tokenizers_0.3.0.tgz
Old sources: tokenizers archive

Reverse dependencies:

Reverse imports: covfefe, deeplr, DeepPINCS, DramaAnalysis, pdfsearch, proustr, rslp, textrecipes, tidypmc, tidytext, ttgsea, wactor, WhatsR
Reverse suggests: edgarWebR, torchdatasets
Reverse enhances: quanteda

Linking:

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