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