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Adding noun_chunks to the DUTCH language model (nl) (#8529)
* ✨ implement noun_chunks for dutch language * copy/paste FR and SV syntax iterators to accomodate UD tags * added tests with dutch text * signed contributor agreement * 🐛 fix noun chunks generator * built from scratch * define noun chunk as a single Noun-Phrase * includes some corner cases debugging (incorrect POS tagging) * test with provided annotated sample (POS, DEP) * ✅ fix failing test * CI pipeline did not like the added sample file * add the sample as a pytest fixture * Update spacy/lang/nl/syntax_iterators.py * Update spacy/lang/nl/syntax_iterators.py Code readability Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com> * Update spacy/tests/lang/nl/test_noun_chunks.py correct comment Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com> * finalize code * change "if next_word" into "if next_word is not None" Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>
This commit is contained in:
Julien Rossi
GitHub
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@ -864,6 +864,9 @@ class Errors:
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E1018 = ("Knowledge base for component '{name}' is not set. "
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"Make sure either `nel.initialize` or `nel.set_kb` "
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"is called with a `kb_loader` function.")
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E1019 = ("`noun_chunks` requires the pos tagging, which requires a "
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"statistical model to be installed and loaded. For more info, see "
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"the documentation:\nhttps://spacy.io/usage/models")
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# Deprecated model shortcuts, only used in errors and warnings
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@ -1,12 +1,14 @@
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from typing import Optional
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from thinc.api import Model
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from .stop_words import STOP_WORDS
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from .lemmatizer import DutchLemmatizer
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from .lex_attrs import LEX_ATTRS
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from .tokenizer_exceptions import TOKENIZER_EXCEPTIONS
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from .punctuation import TOKENIZER_PREFIXES, TOKENIZER_INFIXES
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from .punctuation import TOKENIZER_SUFFIXES
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from .lemmatizer import DutchLemmatizer
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from .stop_words import STOP_WORDS
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from .syntax_iterators import SYNTAX_ITERATORS
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from .tokenizer_exceptions import TOKENIZER_EXCEPTIONS
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from ...language import Language
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@ -16,6 +18,7 @@ class DutchDefaults(Language.Defaults):
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infixes = TOKENIZER_INFIXES
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suffixes = TOKENIZER_SUFFIXES
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lex_attr_getters = LEX_ATTRS
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syntax_iterators = SYNTAX_ITERATORS
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stop_words = STOP_WORDS
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72
spacy/lang/nl/syntax_iterators.py
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72
spacy/lang/nl/syntax_iterators.py
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@ -0,0 +1,72 @@
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from typing import Union, Iterator
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from ...symbols import NOUN, PRON
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from ...errors import Errors
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from ...tokens import Doc, Span
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def noun_chunks(doclike: Union[Doc, Span]) -> Iterator[Span]:
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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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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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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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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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@ -202,6 +202,11 @@ def ne_tokenizer():
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return get_lang_class("ne")().tokenizer
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@pytest.fixture(scope="session")
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def nl_vocab():
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return get_lang_class("nl")().vocab
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@pytest.fixture(scope="session")
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def nl_tokenizer():
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return get_lang_class("nl")().tokenizer
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209
spacy/tests/lang/nl/test_noun_chunks.py
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209
spacy/tests/lang/nl/test_noun_chunks.py
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@ -0,0 +1,209 @@
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from spacy.tokens import Doc
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import pytest
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@pytest.fixture
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def nl_sample(nl_vocab):
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# TEXT :
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# Haar vriend lacht luid. We kregen alweer ruzie toen we de supermarkt ingingen.
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# Aan het begin van de supermarkt is al het fruit en de groentes. Uiteindelijk hebben we dan ook
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# geen avondeten gekocht.
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words = [
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"Haar",
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"vriend",
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"lacht",
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"luid",
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".",
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"We",
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"kregen",
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"alweer",
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"ruzie",
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"toen",
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"we",
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"de",
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"supermarkt",
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"ingingen",
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".",
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"Aan",
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"het",
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"begin",
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"van",
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"de",
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"supermarkt",
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"is",
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"al",
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"het",
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"fruit",
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"en",
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"de",
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"groentes",
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".",
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"Uiteindelijk",
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"hebben",
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"we",
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"dan",
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"ook",
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"geen",
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"avondeten",
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"gekocht",
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".",
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]
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heads = [
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1,
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2,
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2,
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2,
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2,
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6,
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6,
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6,
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6,
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13,
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13,
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12,
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13,
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6,
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6,
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17,
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17,
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24,
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20,
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20,
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17,
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24,
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24,
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24,
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24,
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27,
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27,
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24,
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24,
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36,
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36,
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36,
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36,
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36,
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35,
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36,
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36,
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36,
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]
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deps = [
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"nmod:poss",
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"nsubj",
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"ROOT",
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"advmod",
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"punct",
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"nsubj",
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"ROOT",
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"advmod",
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"obj",
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"mark",
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"nsubj",
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"det",
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"obj",
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"advcl",
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"punct",
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"case",
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"det",
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"obl",
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"case",
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"det",
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"nmod",
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"cop",
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"advmod",
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"det",
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"ROOT",
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"cc",
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"det",
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"conj",
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"punct",
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"advmod",
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"aux",
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"nsubj",
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"advmod",
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"advmod",
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"det",
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"obj",
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"ROOT",
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"punct",
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]
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pos = [
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"PRON",
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"NOUN",
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"VERB",
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"ADJ",
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"PUNCT",
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"PRON",
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"VERB",
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"ADV",
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"NOUN",
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"SCONJ",
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"PRON",
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"DET",
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"NOUN",
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"NOUN",
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"PUNCT",
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"ADP",
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"DET",
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"NOUN",
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"ADP",
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"DET",
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"NOUN",
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"AUX",
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"ADV",
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"DET",
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"NOUN",
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"CCONJ",
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"DET",
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"NOUN",
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"PUNCT",
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"ADJ",
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"AUX",
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"PRON",
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"ADV",
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"ADV",
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"DET",
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"NOUN",
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"VERB",
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"PUNCT",
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]
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return Doc(nl_vocab, words=words, heads=heads, deps=deps, pos=pos)
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@pytest.fixture
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def nl_reference_chunking():
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# Using frog https://github.com/LanguageMachines/frog/ we obtain the following NOUN-PHRASES:
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return [
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"haar vriend",
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"we",
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"ruzie",
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"we",
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"de supermarkt",
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"het begin",
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"de supermarkt",
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"het fruit",
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"de groentes",
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"we",
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"geen avondeten",
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]
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def test_need_dep(nl_tokenizer):
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"""
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Test that noun_chunks raises Value Error for 'nl' language if Doc is not parsed.
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"""
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txt = "Haar vriend lacht luid."
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doc = nl_tokenizer(txt)
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with pytest.raises(ValueError):
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list(doc.noun_chunks)
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def test_chunking(nl_sample, nl_reference_chunking):
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"""
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Test the noun chunks of a sample text. Uses a sample.
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The sample text simulates a Doc object as would be produced by nl_core_news_md.
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"""
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chunks = [s.text.lower() for s in nl_sample.noun_chunks]
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assert chunks == nl_reference_chunking
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