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42 lines
1.6 KiB
42 lines
1.6 KiB
from typing import Iterator, Set, Tuple, Union
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from ...symbols import NOUN, PRON, PROPN, VERB
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from ...tokens import Doc, Span
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# TODO: this can probably be pruned a bit
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# fmt: off
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labels = ["nsubj", "nmod", "ddoclike", "nsubjpass", "pcomp", "pdoclike", "doclike", "obl", "dative", "appos", "attr", "ROOT"]
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# fmt: on
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def noun_chunks(doclike: Union[Doc, Span]) -> Iterator[Tuple[int, int, int]]:
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"""Detect base noun phrases from a dependency parse. Works on Doc and Span."""
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doc = doclike.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[int] = set()
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for i, word in enumerate(doclike):
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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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