mirror of
https://github.com/explosion/spaCy.git
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e2b70df012
* Use isort with Black profile * isort all the things * Fix import cycles as a result of import sorting * Add DOCBIN_ALL_ATTRS type definition * Add isort to requirements * Remove isort from build dependencies check * Typo
76 lines
2.8 KiB
Python
76 lines
2.8 KiB
Python
from typing import Iterator, Tuple, Union
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from ...errors import Errors
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from ...symbols import NOUN, PRON
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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 Doc and Span.
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The definition is inspired by https://www.nltk.org/book/ch07.html
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Consider : [Noun + determinant / adjective] and also [Pronoun]
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"""
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# fmt: off
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# labels = ["nsubj", "nsubj:pass", "obj", "iobj", "ROOT", "appos", "nmod", "nmod:poss"]
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# fmt: on
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doc = doclike.doc # Ensure works on both Doc and Span.
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# Check for dependencies: POS, DEP
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if not doc.has_annotation("POS"):
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raise ValueError(Errors.E1019)
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if not doc.has_annotation("DEP"):
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raise ValueError(Errors.E029)
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# See UD tags: https://universaldependencies.org/u/dep/index.html
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# amod = adjectival modifier
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# nmod:poss = possessive nominal modifier
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# nummod = numeric modifier
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# det = determiner
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# det:poss = possessive determiner
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noun_deps = [
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doc.vocab.strings[label] for label in ["amod", "nmod:poss", "det", "det:poss"]
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]
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# nsubj = nominal subject
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# nsubj:pass = passive nominal subject
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pronoun_deps = [doc.vocab.strings[label] for label in ["nsubj", "nsubj:pass"]]
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# Label NP for the Span to identify it as Noun-Phrase
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span_label = doc.vocab.strings.add("NP")
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# Only NOUNS and PRONOUNS matter
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end_span = -1
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for i, word in enumerate(filter(lambda x: x.pos in [PRON, NOUN], doclike)):
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# For NOUNS
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# Pick children from syntactic parse (only those with certain dependencies)
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if word.pos == NOUN:
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# Some debugging. It happens that VERBS are POS-TAGGED as NOUNS
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# We check if the word has a "nsubj", if it's the case, we eliminate it
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nsubjs = filter(
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lambda x: x.dep == doc.vocab.strings["nsubj"], word.children
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)
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next_word = next(nsubjs, None)
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if next_word is not None:
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# We found some nsubj, so we skip this word. Otherwise, consider it a normal NOUN
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continue
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children = filter(lambda x: x.dep in noun_deps, word.children)
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children_i = [c.i for c in children] + [word.i]
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start_span = min(children_i)
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if start_span >= end_span:
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end_span = max(children_i) + 1
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yield start_span, end_span, span_label
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# PRONOUNS only if it is the subject of a verb
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elif word.pos == PRON:
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if word.dep in pronoun_deps:
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start_span = word.i
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if start_span >= end_span:
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end_span = word.i + 1
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yield start_span, end_span, span_label
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SYNTAX_ITERATORS = {"noun_chunks": noun_chunks}
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