Replace entity with its label in SpaCy - nlp

Question

Is there anyway by SpaCy to replace entity detected by SpaCy NER with its label?
For example:
I am eating an apple while playing with my Apple Macbook.
I have trained NER model with SpaCy to detect "FRUITS" entity and the model successfully detects the first "apple" as "FRUITS", but not the second "Apple".
I want to do post-processing of my data by replacing each entity with its label, so I want to replace the first "apple" with "FRUITS". The sentence will be "I am eating an FRUITS while playing with my Apple Macbook."
If I simply use regex, it will replace the second "Apple" with "FRUITS" as well, which is incorrect. Is there any smart way to do this?
Thanks!

Answer 1

the entity label is an attribute of the token (see here)
import spacy
from spacy import displacy
nlp = spacy.load('en_core_web_lg')
s = "His friend Nicolas is here."
doc = nlp(s)
print([t.text if not t.ent_type_ else t.ent_type_ for t in doc])
# ['His', 'friend', 'PERSON', 'is', 'here', '.']
print(" ".join([t.text if not t.ent_type_ else t.ent_type_ for t in doc]) )
# His friend PERSON is here .
Edit:
In order to handle cases were entities can span several words the following code can be used instead:
s = "His friend Nicolas J. Smith is here with Bart Simpon and Fred."
doc = nlp(s)
newString = s
for e in reversed(doc.ents): #reversed to not modify the offsets of other entities when substituting
start = e.start_char
end = start + len(e.text)
newString = newString[:start] + e.label_ + newString[end:]
print(newString)
#His friend PERSON is here with PERSON and PERSON.
Update:
Jinhua Wang brought to my attention that there is now a more built-in and simpler way to do this using the merge_entities pipe.
See Jinhua's answer below.

Answer 2

A more elegant modification to #DBaker's solution above when entities can span several words:
import spacy
from spacy import displacy
nlp = spacy.load('en_core_web_lg')
nlp.add_pipe("merge_entities")
s = "His friend Nicolas J. Smith is here with Bart Simpon and Fred."
doc = nlp(s)
print([t.text if not t.ent_type_ else t.ent_type_ for t in doc])
# ['His', 'friend', 'PERSON', 'is', 'here', 'with', 'PERSON', 'and', 'PERSON', '.']
print(" ".join([t.text if not t.ent_type_ else t.ent_type_ for t in doc]) )
# His friend PERSON is here with PERSON and PERSON .
You can check the documentation on Spacy here. It uses the built in Pipeline for the job and has good support for multiprocessing. I believe this is the officially supported way to replace entities by their tags.

Answer 3

A slightly shorter version of #DBaker answer which uses end_char instead of computing it:
for ent in reversed(doc.ents):
text = text[:ent.start_char] + ent.label_ + text[ent.end_char:]