The statistical pipeline components like ner provide their labels under .labels:

import spacy
nlp = spacy.load("en_core_web_sm")
nlp.get_pipe("ner").labels

Hey atalnarayan,

In general the approach you are taking seems to be on the right track, but your question is a bit general for a discussion here. Let me point you to some relevant material:

1. Finding video game titles with sense2vec: https://www.youtube.com/watch?v=EoYHbUHr0fM
2. Detailed example about using the entity ruler to find museum names: https://www.youtube.com/watch?v=Ds18bQAzygo.
3. Rather than the EntityRuler we recommend using the SpanRuler in the future: https://spacy.io/api/spanruler
4. Using dependency tree for extracting information: https://www.youtube.com/watch?v=BoyLPiXXEYA&t=429s.
5. For more in-depth information about entity extraction I recommend this Chapter: https://web.stanford.edu/~jurafsky/slp3/8.pdf
6. For practical examples for machine learning based named entity recognition with spacy you can checkout the relevant projects here: https://github.com/explosion/projects.

This will not handle entities covering multiple tokens.

import spacy
nlp = spacy.load('en_core_web_sm')
text_data = 'New York is in USA'
document = nlp(text_data)

text_no_namedentities = []
ents = [e.text for e in document.ents]
for item in document:
    if item.text in ents:
        pass
    else:
        text_no_namedentities.append(item.text)
print(" ".join(text_no_namedentities))

Output

'New York is in'

Here USA is correctly removed but couldn't eliminate New York

Solution

import spacy
nlp = spacy.load('en_core_web_sm')
text_data = 'New York is in USA'
document = nlp(text_data)
print(" ".join([ent.text for ent in document if not ent.ent_type_]))

Output

'is in'