Removing named entities from a document using spacy

Question

I have tried to remove words from a document that are considered to be named entities by spacy, so basically removing "Sweden" and "Nokia" from the string example. I could not find a way to work around the problem that entities are stored as a span. So when comparing them with single tokens from a spacy doc, it prompts an error.

In a later step, this process is supposed to be a function applied to several text documents stored in a pandas data frame.

I would appreciate any kind of help and advice on how to maybe better post questions as this is my first one here.

nlp = spacy.load('en')

text_data = u'This is a text document that speaks about entities like Sweden and Nokia'

document = nlp(text_data)

text_no_namedentities = []

for word in document:
    if word not in document.ents:
        text_no_namedentities.append(word)

return " ".join(text_no_namedentities)

It creates the following error:

TypeError: Argument 'other' has incorrect type (expected spacy.tokens.token.Token, got spacy.tokens.span.Span)

Answer 1

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'

Answer 2

This will get you the result you're asking for. Reviewing the Named Entity Recognition should help you going forward.

import spacy

nlp = spacy.load('en_core_web_sm')

text_data = 'This is a text document that speaks about entities like Sweden and Nokia'

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:

This is a text document that speaks about entities like and