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# coding: utf8
from __future__ import unicode_literals
from ...symbols import NOUN, PROPN, PRON
def noun_chunks(obj):
"""
Detect base noun phrases from a dependency parse. Works on both Doc and Span.
"""
labels = ['nsubj', 'dobj', 'nsubjpass', 'pcomp', 'pobj', 'dative', 'appos',
'attr', 'ROOT']
doc = obj.doc # Ensure works on both Doc and Span.
np_deps = [doc.vocab.strings.add(label) for label in labels]
conj = doc.vocab.strings.add('conj')
np_label = doc.vocab.strings.add('NP')
seen = set()
for i, word in enumerate(obj):
if word.pos not in (NOUN, PROPN, PRON):
continue
# Prevent nested chunks from being produced
if word.i in seen:
continue
if word.dep in np_deps:
if any(w.i in seen for w in word.subtree):
continue
seen.update(j for j in range(word.left_edge.i, word.i+1))
yield word.left_edge.i, word.i+1, np_label
elif word.dep == conj:
head = word.head
while head.dep == conj and head.head.i < head.i:
head = head.head
# If the head is an NP, and we're coordinated to it, we're an NP
if head.dep in np_deps:
if any(w.i in seen for w in word.subtree):
continue
seen.update(j for j in range(word.left_edge.i, word.i+1))
yield word.left_edge.i, word.i+1, np_label
SYNTAX_ITERATORS = {
'noun_chunks': noun_chunks
}