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Add (noun chunks) syntax iterators for Danish (#6246)
* add syntax iterators for danish * add test noun chunks for danish syntax iterators * add contributor agreement * update da syntax iterators to remove nested chunks * add tests for da noun chunks * Fix test * add missing import * fix example * Prevent overlapping noun chunks Prevent overlapping noun chunks by tracking the end index of the previous noun chunk span. Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
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.github/contributors/ophelielacroix.md
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# spaCy contributor agreement
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This spaCy Contributor Agreement (**"SCA"**) is based on the
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The SCA applies to any contribution that you make to any product or project
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## Contributor Agreement
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## Contributor Details
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| Field | Entry |
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|-------------------------------|-----------------|
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| Name | Ophélie Lacroix |
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| Company name (if applicable) | |
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| Title or role (if applicable) | |
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| Date | |
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| GitHub username | ophelielacroix |
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| Website (optional) | |
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@ -7,6 +7,7 @@ from .stop_words import STOP_WORDS
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from .lex_attrs import LEX_ATTRS
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from .morph_rules import MORPH_RULES
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from ..tag_map import TAG_MAP
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from .syntax_iterators import SYNTAX_ITERATORS
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from ..tokenizer_exceptions import BASE_EXCEPTIONS
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from ...language import Language
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@ -24,6 +25,7 @@ class DanishDefaults(Language.Defaults):
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suffixes = TOKENIZER_SUFFIXES
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tag_map = TAG_MAP
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stop_words = STOP_WORDS
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syntax_iterators = SYNTAX_ITERATORS
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class Danish(Language):
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spacy/lang/da/syntax_iterators.py
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spacy/lang/da/syntax_iterators.py
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# coding: utf8
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from __future__ import unicode_literals
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from ...symbols import NOUN, PROPN, PRON, VERB, AUX
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from ...errors import Errors
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def noun_chunks(doclike):
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def is_verb_token(tok):
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return tok.pos in [VERB, AUX]
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def next_token(tok):
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try:
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return tok.nbor()
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except IndexError:
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return None
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def get_left_bound(doc, root):
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left_bound = root
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for tok in reversed(list(root.lefts)):
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if tok.dep in np_left_deps:
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left_bound = tok
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return left_bound
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def get_right_bound(doc, root):
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right_bound = root
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for tok in root.rights:
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if tok.dep in np_right_deps:
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right = get_right_bound(doc, tok)
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if list(
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filter(
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lambda t: is_verb_token(t) or t.dep in stop_deps,
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doc[root.i : right.i],
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)
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):
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break
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else:
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right_bound = right
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return right_bound
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def get_bounds(doc, root):
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return get_left_bound(doc, root), get_right_bound(doc, root)
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doc = doclike.doc
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if not doc.is_parsed:
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raise ValueError(Errors.E029)
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if not len(doc):
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return
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left_labels = [
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"det",
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"fixed",
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"nmod:poss",
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"amod",
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"flat",
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"goeswith",
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"nummod",
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"appos",
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]
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right_labels = ["fixed", "nmod:poss", "amod", "flat", "goeswith", "nummod", "appos"]
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stop_labels = ["punct"]
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np_label = doc.vocab.strings.add("NP")
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np_left_deps = [doc.vocab.strings.add(label) for label in left_labels]
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np_right_deps = [doc.vocab.strings.add(label) for label in right_labels]
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stop_deps = [doc.vocab.strings.add(label) for label in stop_labels]
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chunks = []
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prev_right = -1
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for token in doclike:
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if token.pos in [PROPN, NOUN, PRON]:
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left, right = get_bounds(doc, token)
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if left.i <= prev_right:
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continue
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yield left.i, right.i + 1, np_label
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prev_right = right.i
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SYNTAX_ITERATORS = {"noun_chunks": noun_chunks}
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spacy/tests/lang/da/test_noun_chunks.py
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spacy/tests/lang/da/test_noun_chunks.py
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# coding: utf-8
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from __future__ import unicode_literals
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import pytest
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from ...util import get_doc
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def test_noun_chunks_is_parsed(da_tokenizer):
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"""Test that noun_chunks raises Value Error for 'da' language if Doc is not parsed.
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To check this test, we're constructing a Doc
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with a new Vocab here and forcing is_parsed to 'False'
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to make sure the noun chunks don't run.
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"""
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doc = da_tokenizer("Det er en sætning")
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doc.is_parsed = False
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with pytest.raises(ValueError):
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list(doc.noun_chunks)
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DA_NP_TEST_EXAMPLES = [
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(
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"Hun elsker at plukker frugt.",
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['PRON', 'VERB', 'PART', 'VERB', 'NOUN', 'PUNCT'],
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['nsubj', 'ROOT', 'mark', 'obj', 'obj', 'punct'],
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[1, 0, 1, -2, -1, -4],
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["Hun", "frugt"],
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),
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(
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"Påfugle er de smukkeste fugle.",
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['NOUN', 'AUX', 'DET', 'ADJ', 'NOUN', 'PUNCT'],
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['nsubj', 'cop', 'det', 'amod', 'ROOT', 'punct'],
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[4, 3, 2, 1, 0, -1],
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["Påfugle", "de smukkeste fugle"],
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),
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(
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"Rikke og Jacob Jensen glæder sig til en hyggelig skovtur",
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['PROPN', 'CCONJ', 'PROPN', 'PROPN', 'VERB', 'PRON', 'ADP', 'DET', 'ADJ', 'NOUN'],
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['nsubj', 'cc', 'conj', 'flat', 'ROOT', 'obj', 'case', 'det', 'amod', 'obl'],
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[4, 1, -2, -1, 0, -1, 3, 2, 1, -5],
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["Rikke", "Jacob Jensen", "sig", "en hyggelig skovtur"],
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),
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]
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@pytest.mark.parametrize(
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"text,pos,deps,heads,expected_noun_chunks", DA_NP_TEST_EXAMPLES
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)
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def test_da_noun_chunks(da_tokenizer, text, pos, deps, heads, expected_noun_chunks):
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tokens = da_tokenizer(text)
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assert len(heads) == len(pos)
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doc = get_doc(
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tokens.vocab, words=[t.text for t in tokens], heads=heads, deps=deps, pos=pos
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)
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noun_chunks = list(doc.noun_chunks)
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assert len(noun_chunks) == len(expected_noun_chunks)
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for i, np in enumerate(noun_chunks):
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assert np.text == expected_noun_chunks[i]
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