I am new to Spacy and I would like to extract "all" the noun phrases from a sentence. I'm wondering how I can do it. I have the following code:
import spacy
nlp = spacy.load("en")
file = open("E:/test.txt", "r")
doc = nlp(file.read())
for np in doc.noun_chunks:
print(np.text)
But it returns only the base noun phrases, that is, phrases which don't have any other NP in them. That is, for the following phrase, I get the result below:
Phrase: We try to explicitly describe the geometry of the edges of the images.
Result: We, the geometry, the edges, the images.
Expected result: We, the geometry, the edges, the images, the geometry of the edges of the images, the edges of the images.
How can I get all the noun phrases, including nested phrases?
Please see commented code below to recursively combine the nouns. Code inspired by the Spacy Docs here
import spacy
nlp = spacy.load("en")
doc = nlp("We try to explicitly describe the geometry of the edges of the images.")
for np in doc.noun_chunks: # use np instead of np.text
print(np)
print()
# code to recursively combine nouns
# 'We' is actually a pronoun but included in your question
# hence the token.pos_ == "PRON" part in the last if statement
# suggest you extract PRON separately like the noun-chunks above
index = 0
nounIndices = []
for token in doc:
# print(token.text, token.pos_, token.dep_, token.head.text)
if token.pos_ == 'NOUN':
nounIndices.append(index)
index = index + 1
print(nounIndices)
for idxValue in nounIndices:
doc = nlp("We try to explicitly describe the geometry of the edges of the images.")
span = doc[doc[idxValue].left_edge.i : doc[idxValue].right_edge.i+1]
span.merge()
for token in doc:
if token.dep_ == 'dobj' or token.dep_ == 'pobj' or token.pos_ == "PRON":
print(token.text)
For every noun chunk you can also get the subtree beneath it.
Spacy provides two ways to access that:left_edge and right edge attributes and
the subtree attribute, which returns a Token iterator rather than a span.
Combining noun_chunks and their subtree lead to some duplication which can be removed later.
Here is an example using the left_edge and right edge attributes
{np.text
for nc in doc.noun_chunks
for np in [
nc,
doc[
nc.root.left_edge.i
:nc.root.right_edge.i+1]]}
==>
{'We',
'the edges',
'the edges of the images',
'the geometry',
'the geometry of the edges of the images',
'the images'}
Please try this to get all nouns from a text:
import spacy
nlp = spacy.load("en_core_web_sm")
text = ("We try to explicitly describe the geometry of the edges of the images.")
doc = nlp(text)
print([chunk.text for chunk in doc.noun_chunks])