cleanNLP (version 1.5.2)

cleanNLP-package: cleanNLP: A Tidy Data Model for Natural Language Processing

Description

Provides a set of fast tools for converting a textual corpus into a set of normalized tables. The underlying natural language processing pipeline utilizes either the Python module spaCy or the Java-based Stanford CoreNLP library. The Python option is faster and generally easier to install; the Java option has additional annotators that are not available in spaCy.

Details

Once the package is set up, run one of init_tokenizers, init_spaCy, or init_coreNLP to load the desired NLP backend. After this function is done running, use annotate to run the annotation engine over a corpus of text. Functions are then available to extract data tables from the annotation object: get_token, get_dependency, get_document, get_coreference, get_entity, get_sentence, and get_vector. See their documentation for further details. The package vignettes provide more detailed set-up information.

If loading annotation that have previously been saved to disk, these can be pulled back into R using read_annotation. This does not require Java or Python nor does it require initializing the annotation pipeline.

See Also

Useful links:

• https://statsmaths.github.io/cleanNLP/

Examples

## Not run: ------------------------------------
# # load the annotation engine (can also use spaCy and coreNLP backends)
# setup_tokenizers_backend()
# init_backend(type = "tokenizers")
# 
# # annotate your text
# annotation <- annotate("path/to/corpus/directory")
# 
# # pull off data tables
# token <- get_token(annotation)
# dependency <- get_dependency(annotation)
# document <- get_document(annotation)
# coreference <- get_coreference(annotation)
# entity <- get_entity(annotation)
# sentiment <- get_sentence(annotation)
# vector <- get_vector(annotation)
## ---------------------------------------------