mirror of https://github.com/explosion/spaCy.git
48 lines
1.6 KiB
Python
48 lines
1.6 KiB
Python
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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 = [
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"nsubj",
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"nsubj:pass",
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"dobj",
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"obj",
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"iobj",
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"ROOT",
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"appos",
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"nmod",
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"nmod:poss",
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]
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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.add("conj")
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np_label = doc.vocab.strings.add("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.right_edge.i + 1))
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yield word.left_edge.i, word.right_edge.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.right_edge.i + 1))
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yield word.left_edge.i, word.right_edge.i + 1, np_label
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SYNTAX_ITERATORS = {"noun_chunks": noun_chunks}
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