Noun phrases with spacy

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.textu'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 toodef iter_nps(doc):    for word in doc:        if word.dep in np_labels:            yield word.subtree

import spacynlp = 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'''

You can also get noun from a sentence like this:

    import spacy    nlp=spacy.load("en_core_web_sm")    doc=nlp("When Sebastian Thrun started working on self-driving cars at "    "Google in 2007, few people outside of the company took him "    "seriously. “I can tell you very senior CEOs of major American "    "car companies would shake my hand and turn away because I wasn’t "    "worth talking to,” said Thrun, in an interview with Recode earlier "    "this week.")    #doc text is from spacy website    for x in doc :    if x.pos_ == "NOUN" or x.pos_ == "PROPN" or x.pos_=="PRON":    print(x)    # here you can get Nouns, Proper Nouns and Pronouns