Reputation: 19405
I am looking at Spacy's code to extract noun chunks (reproduced below) and I do not understand the part that comments:
Prevent nested chunks from being produced
for i, word in enumerate(doclike):
if word.pos not in (NOUN, PROPN, PRON):
continue
# Prevent nested chunks from being produced
if word.left_edge.i <= prev_end:
continue
I get that we are trying to avoid nested chunks, but could someone please explain to me how this is achieved with the left_edge
methods? How is this keeping track of the start/ending index of the noun-chunk
?
Thanks!
# coding: utf8
from __future__ import unicode_literals
from ...symbols import NOUN, PROPN, PRON
from ...errors import Errors
def noun_chunks(doclike):
"""
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 = doclike.doc # Ensure works on both Doc and Span.
if not doc.is_parsed:
raise ValueError(Errors.E029)
np_deps = [doc.vocab.strings.add(label) for label in labels]
conj = doc.vocab.strings.add("conj")
np_label = doc.vocab.strings.add("NP")
prev_end = -1
for i, word in enumerate(doclike):
if word.pos not in (NOUN, PROPN, PRON):
continue
# Prevent nested chunks from being produced
if word.left_edge.i <= prev_end:
continue
if word.dep in np_deps:
prev_end = word.i
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:
prev_end = word.i
yield word.left_edge.i, word.i + 1, np_label
SYNTAX_ITERATORS = {"noun_chunks": noun_chunks}
Upvotes: 2
Views: 640
Reputation: 4359
Valid noun chunks can be part of larger noun chunks. Example:
>>> list(nlp("We went to the clean grocery store").noun_chunks)
[We, the clean grocery store]
>>> list(nlp("We went to clean grocery store").noun_chunks)
[We, clean grocery store]
>>> list(nlp("We went to grocery store").noun_chunks)
[We, grocery store]
So the code you ask about is preventing list(nlp("We went to the clean grocery store").noun_chunks)
from returning [We, the clean grocery store, clean grocery store, grocery store]
Upvotes: 3