59 lines
1.8 KiB
Python
59 lines
1.8 KiB
Python
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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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"""
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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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# Please see documentation for Turkish NP structure
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labels = [
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"nsubj",
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"iobj",
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"obj",
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"obl",
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"appos",
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"orphan",
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"dislocated",
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"ROOT",
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]
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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_deps = [doc.vocab.strings.add(label) for label in labels]
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conj = doc.vocab.strings.add("conj")
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flat = doc.vocab.strings.add("flat")
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np_label = doc.vocab.strings.add("NP")
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def extend_right(w): # Playing a trick for flat
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rindex = w.i + 1
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for rdep in doc[w.i].rights: # Extend the span to right if there is a flat
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if rdep.dep == flat and rdep.pos in (NOUN, PROPN):
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rindex = rdep.i + 1
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else:
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break
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return rindex
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prev_end = len(doc) + 1
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for i, word in reversed(list(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 >= prev_end:
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continue
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if word.dep in np_deps:
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prev_end = word.left_edge.i
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yield word.left_edge.i, extend_right(word), np_label
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elif word.dep == conj:
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cc_token = word.left_edge
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prev_end = cc_token.i
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# Shave off cc tokens from the NP
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yield cc_token.right_edge.i + 1, extend_right(word), np_label
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SYNTAX_ITERATORS = {"noun_chunks": noun_chunks}
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