Noun phrases with spacy

Question

How can I extract noun phrases from text using spacy?
I am not referring to part of speech tags. In the documentation I cannot find anything about noun phrases or regul

Answer 1

If you want base NPs, i.e. NPs without coordination, prepositional phrases or relative clauses, you can use the noun_chunks iterator on the Doc and Span objects:

>>> from spacy.en import English
>>> nlp = English()
>>> doc = nlp(u'The cat and the dog sleep in the basket near the door.')
>>> for np in doc.noun_chunks:
>>>     np.text
u'The cat'
u'the dog'
u'the basket'
u'the door'

If you need something else, the best way is to iterate over the words of the sentence and consider the syntactic context to determine whether the word governs the phrase-type you want. If it does, yield its subtree:

from spacy.symbols import *

np_labels = set([nsubj, nsubjpass, dobj, iobj, pobj]) # Probably others too
def iter_nps(doc):
    for word in doc:
        if word.dep in np_labels:
            yield word.subtree

Answer 2

import spacy
nlp = spacy.load("en_core_web_sm")
doc =nlp('Bananas are an excellent source of potassium.')
for np in doc.noun_chunks:
    print(np.text)
'''
  Bananas
  an excellent source
  potassium
'''

for word in doc:
    print('word.dep:', word.dep, ' | ', 'word.dep_:', word.dep_)
'''
  word.dep: 429  |  word.dep_: nsubj
  word.dep: 8206900633647566924  |  word.dep_: ROOT
  word.dep: 415  |  word.dep_: det
  word.dep: 402  |  word.dep_: amod
  word.dep: 404  |  word.dep_: attr
  word.dep: 443  |  word.dep_: prep
  word.dep: 439  |  word.dep_: pobj
  word.dep: 445  |  word.dep_: punct
'''

from spacy.symbols import *
np_labels = set([nsubj, nsubjpass, dobj, iobj, pobj])
print('np_labels:', np_labels)
'''
  np_labels: {416, 422, 429, 430, 439}
'''

https://www.geeksforgeeks.org/use-yield-keyword-instead-return-keyword-python/

def iter_nps(doc):
    for word in doc:
        if word.dep in np_labels:
            yield(word.dep_)

iter_nps(doc)
'''
  <generator object iter_nps at 0x7fd7b08b5bd0>
'''

## Modified method:
def iter_nps(doc):
    for word in doc:
        if word.dep in np_labels:
            print(word.text, word.dep_)

iter_nps(doc)
'''
  Bananas nsubj
  potassium pobj
'''

doc = nlp('BRCA1 is a tumor suppressor protein that functions to maintain genomic stability.')
for np in doc.noun_chunks:
    print(np.text)
'''
  BRCA1
  a tumor suppressor protein
  genomic stability
'''

iter_nps(doc)
'''
  BRCA1 nsubj
  that nsubj
  stability dobj
'''

Answer 3

If you want to specify more exactly which kind of noun phrase you want to extract, you can use textacy's matches function. You can pass any combination of POS tags. For example,

textacy.extract.matches(doc, "POS:ADP POS:DET:? POS:ADJ:? POS:NOUN:+")

will return any nouns that are preceded by a preposition and optionally by a determiner and/or adjective.

Textacy was built on spacy, so they should work perfectly together.