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. Users may make use of the 'udpipe' back end with
no external dependencies, or two Python back ends with 'spaCy'
or 'CoreNLP' .
Exposed annotation tasks include tokenization, part of speech tagging, named
entity recognition, and dependency parsing.