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.
R package providing annotators and a tidy data model for natural language processing
This is a major re-structuring of the cleanNLP package. The primary changes include:
the new udpipe backend, which gives tokenization, POS-tags, lemmatization,and dependency parsing with no external dependencies
all functions return results in memory; to store data on disk, users need to save the output manually
use a character vector named 'id' as the document id (it was previously an integer index); this is to conform to the text interchange format (tif)
functions now use the prefix 'cnlp_', following the convention of packages such as stringi
the cnlp_get_tfidf function now returns a named sparse matrix in lieu of a named list
There are also many internal changes, primarily to deal with the new spaCy (2.0) version and to make the use of udpipe more natural.
In this version, the internal mechanisms for running the tokenizers backend have been changed. We are now directly calling the stringi functions with options that better mimic those of the the spaCy and CoreNLP backends. Despite the lack of dependency on the tokenizers package, we will continue to use the name "tokenizers" for the backend to maintain backwards consistency.
As part of the change to custom stringi function, we now also support setting the locale as part of initalizing the tokenizers backend. This allows for an easy way of tokenizing text where custom spaCy or coreNLP models do not yet exist.
There is currently a pre-release version of spaCy version 2.0.0. The current version has been tested and runs smoothly with cleanNLP. The new neural network models are sufficently faster and more accurate; we suggest migrating to the version 2 series as it becomes stable for production.
This version contains many internal changes to the way that external libraries are called and referenced in order to comply with goodpractice::gp(). Two important user-facing changes include:
annotate has been changed to
run_annotations in order to avoid a conflict
get_token has new options for producing
a single joined table with dependencies and entities.
This should make it easier to work with the output for
users needed more than lemmas and POS-tags but not
requiring deeper table joins.
This update contains several major changes, include:
document and sentence ids now start at 1
download function checks and warns if Java files are already downloaded
table joins inside of get_document() no longer produce verbose output
get_token() now has an option, FALSE by default, for whether sentence ROOTS should be returned
the speed parameter to init_coreNLP() has been renamed as anno_level