I am parsing a sentence with Spacy like following:
import spacy
nlp = spacy.load(\"en\")
span = nlp(\"This is some text.\")
I am wondering
The other answer requires you to lose POS information.
def remove_i_element_from_span(span, index):
nlp_list = list(span)
del nlp_list[index]
return nlp(" ".join([e.text for e in nlp_list]))
There is a workaround for that.
The idea is that you create a numpy array from the doc, you delete the entry you don't want and then you create a doc from the new numpy array.
import spacy
from spacy.attrs import LOWER, POS, ENT_TYPE, IS_ALPHA
from spacy.tokens import Doc
import numpy
def remove_span(doc, index):
np_array = doc.to_array([LOWER, POS, ENT_TYPE, IS_ALPHA])
np_array_2 = numpy.delete(np_array, (index), axis = 0)
doc2 = Doc(doc.vocab, words=[t.text for i, t in enumerate(doc) if i!=index])
doc2.from_array([LOWER, POS, ENT_TYPE, IS_ALPHA], np_array_2)
return doc2
# load english model
nlp = spacy.load('en')
doc = nlp("This is some text")
new_doc = remove_span(doc, 3)
print(new_doc)
Hope it helps!