mirror of https://github.com/explosion/spaCy.git
55 lines
1.5 KiB
Python
55 lines
1.5 KiB
Python
from ...symbols import NOUN, PROPN, PRON, VERB
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# XXX this can probably be pruned a bit
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labels = [
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"nsubj",
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"nmod",
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"dobj",
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"nsubjpass",
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"pcomp",
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"pobj",
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"obj",
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"obl",
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"dative",
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"appos",
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"attr",
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"ROOT",
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]
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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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doc = obj.doc # Ensure works on both Doc and Span.
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np_deps = [doc.vocab.strings.add(label) for label in labels]
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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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unseen = [w.i for w in word.subtree if w.i not in seen]
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if not unseen:
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continue
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# this takes care of particles etc.
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seen.update(j.i for j in word.subtree)
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# This avoids duplicating embedded clauses
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seen.update(range(word.i + 1))
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# if the head of this is a verb, mark that and rights seen
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# Don't do the subtree as that can hide other phrases
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if word.head.pos == VERB:
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seen.add(word.head.i)
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seen.update(w.i for w in word.head.rights)
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yield unseen[0], word.i + 1, np_label
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SYNTAX_ITERATORS = {"noun_chunks": noun_chunks}
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