We've discussed the incredible power and simplicity of spaCy before when we talked about POS-tagging – and we will cite the same reasons when using spaCy for NER-tagging. In practice, if you've followed the previous chapter on POS-tagging, we have already finished our POS-tagging; since NER-tagging is part of spaCy's natural pipeline, simply processing a document with the pipeline means that along with being tokenized and POS-tagged, it has already been NER-tagged (not to mention dependency parsed too!).
Setting up our model involves the same steps we saw before.
import spacy
nlp = spacy.load('en')
Let's now decide some sentences we would like to NER-tag.
sent_0 = nlp(u'Donald Trump visited at the government headquarters ...
