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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.

Functions (12)

cleanNLP-package

cleanNLP: A Tidy Data Model for Natural Language Processing

cnlp_annotate

Run the annotation pipeline on a set of documents

cnlp_download_corenlp

Download model files needed for coreNLP

cnlp_download_spacy

Download model files needed for spacy

cnlp_init_corenlp

Interface for initializing the coreNLP backend

cnlp_init_spacy

Interface for initializing the spacy backend

cnlp_init_stringi

Interface for initializing the standard R backend

cnlp_init_udpipe

Interface for initializing the udpipe backend

cnlp_utils_pca

Compute Principal Components and store as a Data Frame

cnlp_utils_tfidf

Construct the TF-IDF Matrix from Annotation or Data Frame

un

Universal Declaration of Human Rights

word_frequency

Most frequent English words

cleanNLP

A Tidy Data Model for Natural Language Processing

v3.0.3
LGPL-2
Authors
Taylor B. Arnold [aut, cre]
Initial release

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