Install error on negspacy (pip install)

[jenojp]

Install error on negspacy (pip install)

[jenojp]

Hi, thank you for raising this issue. Please try installing again and let me know if you continue to get the issue.

pip install negspacy --upgrade

I think I fixed the windows decoding issue.

[jenojp]

Replicated the issue on azure and had no issue when installing the new version. Let me know if you're still having the problem. Thanks!

[GB-TA]

Thank Jeno for fixing the issue with negspacy install. I was able to install negspacy and import the packages.

Ran:
nlp = spacy.load("en_core_web_sm")
negex = Negex(nlp)
nlp.add_pipe(negex, last=True)
Im trying to see how negation works with different scenarios:
doc = nlp(" Client is not on the Terrorists watchlist.")
Result:
Terrorists True
doc = nlp(" Client is not on the Terrorists Watch list.")
Result:
Terrorists True
doc = nlp(" Client is not on the Terrorist Watchlist.")
Result : NULL
doc = nlp(" Client is not on the Terrorists watch list.")
Result : NULL
doc = nlp(" Client is not on the terrorists watch list.")
Result : NULL

doc = nlp("Client is not affiliated to Soverign citizen.")
Result : Soverign True
doc = nlp("Client is not affiliated to soverign citizen.")
Result : NULL

Could you please check and explain why the negation detection is failing with some texts which is very close to how the successful ones are written? Is there a NER dictionary that is being being used behind the seen to identify Org entities?
Please let me know if there is a way to fix this

[jenojp]

Oh good! I'm glad that issue is resolved.

Yes - You're using the built in spaCy NER in the backend. If you have a specific dictionary of entities you'd like to process, I would suggest using the EntityRuler in spaCy. Just make sure it's added "ahead" of the negation component in the processing pipeline.

This way you could make your own new entities or add patterns to existing ones.

import spacy
from spacy.pipeline import EntityRuler
from negspacy.negation import Negex

nlp = spacy.load("en_core_web_sm")
ruler = EntityRuler(nlp)
patterns = [{"label": "MYENT", "pattern": [{"LOWER": "sovereign"}, {"LOWER": "citizen"}]}]
# add others in patterns list

ruler.add_patterns(patterns)
nlp.add_pipe(ruler)

negex = Negex(nlp)
nlp.add_pipe(negex, last=True)

doc = nlp("Client is not affiliated to sovereign citizen.")

 for e in doc.ents:
    print(e.text, e._.negex)

[GB-TA]

Thank you! Looking forward to enhancements on this. Great work with this package!