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, a Python back
end with 'spaCy' or the Java back end 'CoreNLP'
. Exposed annotation tasks include
tokenization, part of speech tagging, named entity recognition, entity linking, sentiment
analysis, dependency parsing, coreference resolution, and word embeddings. Summary
statistics regarding token unigram, part of speech tag, and dependency type frequencies
are also included to assist with analyses.