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44 lines
1.8 KiB
44 lines
1.8 KiB
from typing import Iterator, Tuple, Union
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from ...errors import Errors
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from ...symbols import NOUN, PRON, PROPN
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from ...tokens import Doc, Span
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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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# this iterator extracts spans headed by NOUNs starting from the left-most
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# syntactic dependent until the NOUN itself for close apposition and
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# measurement construction, the span is sometimes extended to the right of
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# the NOUN. Example: "eine Tasse Tee" (a cup (of) tea) returns "eine Tasse Tee"
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# and not just "eine Tasse", same for "das Thema Familie".
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# fmt: off
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labels = ["sb", "oa", "da", "nk", "mo", "ag", "ROOT", "root", "cj", "pd", "og", "app"]
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# fmt: on
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doc = doclike.doc # Ensure works on both Doc and Span.
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if not doc.has_annotation("DEP"):
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raise ValueError(Errors.E029)
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np_label = doc.vocab.strings.add("NP")
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np_deps = set(doc.vocab.strings.add(label) for label in labels)
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close_app = doc.vocab.strings.add("nk")
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rbracket = 0
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prev_end = -1
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for i, word in enumerate(doclike):
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if i < rbracket:
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continue
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# Prevent nested chunks from being produced
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if word.left_edge.i <= prev_end:
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continue
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if word.pos in (NOUN, PROPN, PRON) and word.dep in np_deps:
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rbracket = word.i + 1
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# try to extend the span to the right
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# to capture close apposition/measurement constructions
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for rdep in doc[word.i].rights:
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if rdep.pos in (NOUN, PROPN) and rdep.dep == close_app:
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rbracket = rdep.i + 1
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prev_end = rbracket - 1
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yield word.left_edge.i, rbracket, np_label
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SYNTAX_ITERATORS = {"noun_chunks": noun_chunks}
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