Comparing NLTK with spaCy NER

Using the same text you used in the first exercise of this chapter, you'll now see the results using spaCy's NER annotator. How will they compare?

The article has been pre-loaded as article. To minimize execution times, you'll be asked to specify the keyword argument disable=['tagger', 'parser', 'matcher'] when loading the spaCy model, because you only care about the entity in this exercise.

Import spacy.
Load the 'en_core_web_sm' model using spacy.load(). Specify the additional keyword arguments disable=['tagger', 'parser', 'matcher'].
Create a spacy document object by passing article into nlp().
Using ent as your iterator variable, iterate over the entities of doc and print out the labels (ent.label_) and text (ent.text).

Exercise

Comparing NLTK with spaCy NER

Instructions