Noun chunks for Italian (#9662)

* added it vocab

* copied portuguese

* added possessive determiner

* added conjed Nps

* added nmoded Nps

* test misc

* more examples

* fixed typo

* fixed parenth

* fixed comma

* comma fix

* added syntax iters

* fix some index problems

* fixed index

* corrected heads for test case

* fixed tets case

* fixed determiner gender

* cleaned left over

* added example with apostophe
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Duygu Altinok 2021-11-23 16:29:25 +01:00 committed by GitHub
parent a1f25412da
commit 25bd9f9d48
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4 changed files with 315 additions and 1 deletions

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@ -6,13 +6,15 @@ from .tokenizer_exceptions import TOKENIZER_EXCEPTIONS
from .punctuation import TOKENIZER_PREFIXES, TOKENIZER_INFIXES
from ...language import Language, BaseDefaults
from .lemmatizer import ItalianLemmatizer
from .syntax_iterators import SYNTAX_ITERATORS
class ItalianDefaults(BaseDefaults):
tokenizer_exceptions = TOKENIZER_EXCEPTIONS
stop_words = STOP_WORDS
prefixes = TOKENIZER_PREFIXES
infixes = TOKENIZER_INFIXES
stop_words = STOP_WORDS
syntax_iterators = SYNTAX_ITERATORS
class Italian(Language):

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@ -0,0 +1,86 @@
from typing import Union, Iterator, Tuple
from ...symbols import NOUN, PROPN, PRON
from ...errors import Errors
from ...tokens import Doc, Span
def noun_chunks(doclike: Union[Doc, Span]) -> Iterator[Tuple[int, int, int]]:
"""
Detect base noun phrases from a dependency parse. Works on both Doc and Span.
"""
labels = [
"nsubj",
"nsubj:pass",
"obj",
"obl",
"obl:agent",
"nmod",
"pcomp",
"appos",
"ROOT",
]
post_modifiers = ["flat", "flat:name", "fixed", "compound"]
dets = ["det", "det:poss"]
doc = doclike.doc # Ensure works on both Doc and Span.
if not doc.has_annotation("DEP"):
raise ValueError(Errors.E029)
np_deps = {doc.vocab.strings.add(label) for label in labels}
np_modifs = {doc.vocab.strings.add(modifier) for modifier in post_modifiers}
np_label = doc.vocab.strings.add("NP")
adj_label = doc.vocab.strings.add("amod")
det_labels = {doc.vocab.strings.add(det) for det in dets}
det_pos = doc.vocab.strings.add("DET")
adp_label = doc.vocab.strings.add("ADP")
conj = doc.vocab.strings.add("conj")
conj_pos = doc.vocab.strings.add("CCONJ")
prev_end = -1
for i, word in enumerate(doclike):
if word.pos not in (NOUN, PROPN, PRON):
continue
# Prevent nested chunks from being produced
if word.left_edge.i <= prev_end:
continue
if word.dep in np_deps:
right_childs = list(word.rights)
right_child = right_childs[0] if right_childs else None
if right_child:
if (
right_child.dep == adj_label
): # allow chain of adjectives by expanding to right
right_end = right_child.right_edge
elif (
right_child.dep in det_labels and right_child.pos == det_pos
): # cut relative pronouns here
right_end = right_child
elif right_child.dep in np_modifs: # Check if we can expand to right
right_end = word.right_edge
else:
right_end = word
else:
right_end = word
prev_end = right_end.i
left_index = word.left_edge.i
left_index = (
left_index + 1 if word.left_edge.pos == adp_label else left_index
)
yield left_index, right_end.i + 1, np_label
elif word.dep == conj:
head = word.head
while head.dep == conj and head.head.i < head.i:
head = head.head
# If the head is an NP, and we're coordinated to it, we're an NP
if head.dep in np_deps:
prev_end = word.i
left_index = word.left_edge.i # eliminate left attached conjunction
left_index = (
left_index + 1 if word.left_edge.pos == conj_pos else left_index
)
yield left_index, word.i + 1, np_label
SYNTAX_ITERATORS = {"noun_chunks": noun_chunks}

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@ -190,6 +190,11 @@ def it_tokenizer():
return get_lang_class("it")().tokenizer
@pytest.fixture(scope="session")
def it_vocab():
return get_lang_class("it")().vocab
@pytest.fixture(scope="session")
def ja_tokenizer():
pytest.importorskip("sudachipy")

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@ -0,0 +1,221 @@
from spacy.tokens import Doc
import pytest
# fmt: off
@pytest.mark.parametrize(
"words,heads,deps,pos,chunk_offsets",
[
# determiner + noun
# un pollo -> un pollo
(
["un", "pollo"],
[1, 1],
["det", "ROOT"],
["DET", "NOUN"],
[(0,2)],
),
# two determiners + noun
# il mio cane -> il mio cane
(
["il", "mio", "cane"],
[2, 2, 2],
["det", "det:poss", "ROOT"],
["DET", "DET", "NOUN"],
[(0,3)],
),
# two determiners, one is after noun. rare usage but still testing
# il cane mio-> il cane mio
(
["il", "cane", "mio"],
[1, 1, 1],
["det", "ROOT", "det:poss"],
["DET", "NOUN", "DET"],
[(0,3)],
),
# relative pronoun
# È molto bello il vestito che hai acquistat -> il vestito, che the dress that you bought is very pretty.
(
["È", "molto", "bello", "il", "vestito", "che", "hai", "acquistato"],
[2, 2, 2, 4, 2, 7, 7, 4],
['cop', 'advmod', 'ROOT', 'det', 'nsubj', 'obj', 'aux', 'acl:relcl'],
['AUX', 'ADV', 'ADJ', 'DET', 'NOUN', 'PRON', 'AUX', 'VERB'],
[(3,5), (5,6)]
),
# relative subclause
# il computer che hai comprato -> il computer, che the computer that you bought
(
['il', 'computer', 'che', 'hai', 'comprato'],
[1, 1, 4, 4, 1],
['det', 'ROOT', 'nsubj', 'aux', 'acl:relcl'],
['DET', 'NOUN', 'PRON', 'AUX', 'VERB'],
[(0,2), (2,3)]
),
# det + noun + adj
# Una macchina grande -> Una macchina grande
(
["Una", "macchina", "grande"],
[1, 1, 1],
["det", "ROOT", "amod"],
["DET", "NOUN", "ADJ"],
[(0,3)],
),
# noun + adj plural
# mucche bianche
(
["mucche", "bianche"],
[0, 0],
["ROOT", "amod"],
["NOUN", "ADJ"],
[(0,2)],
),
# det + adj + noun
# Una grande macchina -> Una grande macchina
(
['Una', 'grande', 'macchina'],
[2, 2, 2],
["det", "amod", "ROOT"],
["DET", "ADJ", "NOUN"],
[(0,3)]
),
# det + adj + noun, det with apostrophe
# un'importante associazione -> un'importante associazione
(
["Un'", 'importante', 'associazione'],
[2, 2, 2],
["det", "amod", "ROOT"],
["DET", "ADJ", "NOUN"],
[(0,3)]
),
# multiple adjectives
# Un cane piccolo e marrone -> Un cane piccolo e marrone
(
["Un", "cane", "piccolo", "e", "marrone"],
[1, 1, 1, 4, 2],
["det", "ROOT", "amod", "cc", "conj"],
["DET", "NOUN", "ADJ", "CCONJ", "ADJ"],
[(0,5)]
),
# determiner, adjective, compound created by flat
# le Nazioni Unite -> le Nazioni Unite
(
["le", "Nazioni", "Unite"],
[1, 1, 1],
["det", "ROOT", "flat:name"],
["DET", "PROPN", "PROPN"],
[(0,3)]
),
# one determiner + one noun + one adjective qualified by an adverb
# alcuni contadini molto ricchi -> alcuni contadini molto ricchi some very rich farmers
(
['alcuni', 'contadini', 'molto', 'ricchi'],
[1, 1, 3, 1],
['det', 'ROOT', 'advmod', 'amod'],
['DET', 'NOUN', 'ADV', 'ADJ'],
[(0,4)]
),
# Two NPs conjuncted
# Ho un cane e un gatto -> un cane, un gatto
(
['Ho', 'un', 'cane', 'e', 'un', 'gatto'],
[0, 2, 0, 5, 5, 0],
['ROOT', 'det', 'obj', 'cc', 'det', 'conj'],
['VERB', 'DET', 'NOUN', 'CCONJ', 'DET', 'NOUN'],
[(1,3), (4,6)]
),
# Two NPs together
# lo scrittore brasiliano Aníbal Machado -> lo scrittore brasiliano, Aníbal Machado
(
['lo', 'scrittore', 'brasiliano', 'Aníbal', 'Machado'],
[1, 1, 1, 1, 3],
['det', 'ROOT', 'amod', 'nmod', 'flat:name'],
['DET', 'NOUN', 'ADJ', 'PROPN', 'PROPN'],
[(0, 3), (3, 5)]
),
# Noun compound, person name and titles
# Dom Pedro II -> Dom Pedro II
(
["Dom", "Pedro", "II"],
[0, 0, 0],
["ROOT", "flat:name", "flat:name"],
["PROPN", "PROPN", "PROPN"],
[(0,3)]
),
# Noun compound created by flat
# gli Stati Uniti
(
["gli", "Stati", "Uniti"],
[1, 1, 1],
["det", "ROOT", "flat:name"],
["DET", "PROPN", "PROPN"],
[(0,3)]
),
# nmod relation between NPs
# la distruzione della città -> la distruzione, città
(
['la', 'distruzione', 'della', 'città'],
[1, 1, 3, 1],
['det', 'ROOT', 'case', 'nmod'],
['DET', 'NOUN', 'ADP', 'NOUN'],
[(0,2), (3,4)]
),
# Compounding by nmod, several NPs chained together
# la prima fabbrica di droga del governo -> la prima fabbrica, droga, governo
(
["la", "prima", "fabbrica", "di", "droga", "del", "governo"],
[2, 2, 2, 4, 2, 6, 2],
['det', 'amod', 'ROOT', 'case', 'nmod', 'case', 'nmod'],
['DET', 'ADJ', 'NOUN', 'ADP', 'NOUN', 'ADP', 'NOUN'],
[(0, 3), (4, 5), (6, 7)]
),
# several NPs
# Traduzione del rapporto di Susana -> Traduzione, rapporto, Susana
(
['Traduzione', 'del', 'rapporto', 'di', 'Susana'],
[0, 2, 0, 4, 2],
['ROOT', 'case', 'nmod', 'case', 'nmod'],
['NOUN', 'ADP', 'NOUN', 'ADP', 'PROPN'],
[(0,1), (2,3), (4,5)]
),
# Several NPs
# Il gatto grasso di Susana e la sua amica -> Il gatto grasso, Susana, sua amica
(
['Il', 'gatto', 'grasso', 'di', 'Susana', 'e', 'la', 'sua', 'amica'],
[1, 1, 1, 4, 1, 8, 8, 8, 1],
['det', 'ROOT', 'amod', 'case', 'nmod', 'cc', 'det', 'det:poss', 'conj'],
['DET', 'NOUN', 'ADJ', 'ADP', 'PROPN', 'CCONJ', 'DET', 'DET', 'NOUN'],
[(0,3), (4,5), (6,9)]
),
# Passive subject
# La nuova spesa è alimentata dal grande conto in banca di Clinton -> Le nuova spesa, grande conto, banca, Clinton
(
['La', 'nuova', 'spesa', 'è', 'alimentata', 'dal', 'grande', 'conto', 'in', 'banca', 'di', 'Clinton'],
[2, 2, 4, 4, 4, 7, 7, 4, 9, 7, 11, 9],
['det', 'amod', 'nsubj:pass', 'aux:pass', 'ROOT', 'case', 'amod', 'obl:agent', 'case', 'nmod', 'case', 'nmod'],
['DET', 'ADJ', 'NOUN', 'AUX', 'VERB', 'ADP', 'ADJ', 'NOUN', 'ADP', 'NOUN', 'ADP', 'PROPN'],
[(0, 3), (6, 8), (9, 10), (11,12)]
),
# Misc
# Ma mentre questo prestito possa ora sembrare gestibile, un improvviso cambiamento delle circostanze potrebbe portare a problemi di debiti -> questo prestiti, un provisso cambiento, circostanze, problemi, debiti
(
['Ma', 'mentre', 'questo', 'prestito', 'possa', 'ora', 'sembrare', 'gestibile', ',', 'un', 'improvviso', 'cambiamento', 'delle', 'circostanze', 'potrebbe', 'portare', 'a', 'problemi', 'di', 'debitii'],
[15, 6, 3, 6, 6, 6, 15, 6, 6, 11, 11, 15, 13, 11, 15, 15, 17, 15, 19, 17],
['cc', 'mark', 'det', 'nsubj', 'aux', 'advmod', 'advcl', 'xcomp', 'punct', 'det', 'amod', 'nsubj', 'case', 'nmod', 'aux', 'ROOT', 'case', 'obl', 'case', 'nmod'],
['CCONJ', 'SCONJ', 'DET', 'NOUN', 'AUX', 'ADV', 'VERB', 'ADJ', 'PUNCT', 'DET', 'ADJ', 'NOUN', 'ADP', 'NOUN', 'AUX', 'VERB', 'ADP', 'NOUN', 'ADP', 'NOUN'],
[(2,4), (9,12), (13,14), (17,18), (19,20)]
)
],
)
# fmt: on
def test_it_noun_chunks(it_vocab, words, heads, deps, pos, chunk_offsets):
doc = Doc(it_vocab, words=words, heads=heads, deps=deps, pos=pos)
assert [(c.start, c.end) for c in doc.noun_chunks] == chunk_offsets
def test_noun_chunks_is_parsed_it(it_tokenizer):
"""Test that noun_chunks raises Value Error for 'it' language if Doc is not parsed."""
doc = it_tokenizer("Sei andato a Oxford")
with pytest.raises(ValueError):
list(doc.noun_chunks)