get_vector

annotation

Word embeddings map each lemma or token into a high-dimensional
vector space. The implementation here uses a 300-dimensional
space. Only available with the spaCy parser.

Provides a set of fast tools for converting a textual corpus into a set of normalized tables. Users may make use of a Python back end with 'spaCy' <https://spacy.io> or the Java back end 'CoreNLP' <http://stanfordnlp.github.io/CoreNLP/>. A minimal back end with no external dependencies is also provided. 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.

Taylor Arnold

cleanNLP

get_vector: Access word embedding vector from an annotation object

Description

Usage

Arguments

Value

References