Adding noun_chunks to the Swedish language model (sv) (#4422)

* Create syntax_iterators.py

Replica of spacy/lang/fr/syntax_iterators.py

* Added import statements for SYNTAX_ITERATORS

* Create gustavengstrom.md

* Added "dobj" to list of labels in noun_chunks method and a test_noun_chunks method to the  Swedish language model.

* Delete README-checkpoint.md


Co-authored-by: Gustav <gustav@davcon.se>
Co-authored-by: Ines Montani <ines@ines.io>
This commit is contained in:
gustavengstrom 2019-10-21 12:57:06 +02:00 committed by Ines Montani
parent b2f88e2060
commit 050e2445a8
4 changed files with 206 additions and 0 deletions

106
.github/contributors/gustavengstrom.md vendored Normal file
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# spaCy contributor agreement
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## Contributor Details
| Field | Entry |
|------------------------------- | -------------------- |
| Name | Gustav Engström |
| Company name (if applicable) | Davcon |
| Title or role (if applicable) | Data scientis |
| Date | 2019-10-10 |
| GitHub username | gustavengstrom |
| Website (optional) | |

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@ -14,6 +14,7 @@ from ..norm_exceptions import BASE_NORMS
from ...language import Language from ...language import Language
from ...attrs import LANG, NORM from ...attrs import LANG, NORM
from ...util import update_exc, add_lookups from ...util import update_exc, add_lookups
from .syntax_iterators import SYNTAX_ITERATORS
class SwedishDefaults(Language.Defaults): class SwedishDefaults(Language.Defaults):
@ -29,6 +30,7 @@ class SwedishDefaults(Language.Defaults):
suffixes = TOKENIZER_SUFFIXES suffixes = TOKENIZER_SUFFIXES
stop_words = STOP_WORDS stop_words = STOP_WORDS
morph_rules = MORPH_RULES morph_rules = MORPH_RULES
syntax_iterators = SYNTAX_ITERATORS
class Swedish(Language): class Swedish(Language):

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# coding: utf8
from __future__ import unicode_literals
from ...symbols import NOUN, PROPN, PRON
def noun_chunks(obj):
"""
Detect base noun phrases from a dependency parse. Works on both Doc and Span.
"""
labels = [
"nsubj",
"nsubj:pass",
"dobj",
"obj",
"iobj",
"ROOT",
"appos",
"nmod",
"nmod:poss",
]
doc = obj.doc # Ensure works on both Doc and Span.
np_deps = [doc.vocab.strings[label] for label in labels]
conj = doc.vocab.strings.add("conj")
np_label = doc.vocab.strings.add("NP")
seen = set()
for i, word in enumerate(obj):
if word.pos not in (NOUN, PROPN, PRON):
continue
# Prevent nested chunks from being produced
if word.i in seen:
continue
if word.dep in np_deps:
if any(w.i in seen for w in word.subtree):
continue
seen.update(j for j in range(word.left_edge.i, word.right_edge.i + 1))
yield word.left_edge.i, word.right_edge.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:
if any(w.i in seen for w in word.subtree):
continue
seen.update(j for j in range(word.left_edge.i, word.right_edge.i + 1))
yield word.left_edge.i, word.right_edge.i + 1, np_label
SYNTAX_ITERATORS = {"noun_chunks": noun_chunks}

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# coding: utf-8
from __future__ import unicode_literals
import pytest
from spacy.lang.sv.syntax_iterators import SYNTAX_ITERATORS
from ...util import get_doc
SV_NP_TEST_EXAMPLES = [
(
"En student läste en bok", # A student read a book
["DET", "NOUN", "VERB", "DET", "NOUN"],
["det", "nsubj", "ROOT", "det", "dobj"],
[1, 1, 0, 1, -2],
["En student", "en bok"],
),
(
"Studenten läste den bästa boken.", # The student read the best book
["NOUN", "VERB", "DET", "ADJ", "NOUN", "PUNCT"],
["nsubj", "ROOT", "det", "amod", "dobj", "punct"],
[1, 0, 2, 1, -3, -4],
["Studenten", "den bästa boken"],
),
(
"De samvetslösa skurkarna hade stulit de största juvelerna på söndagen", # The remorseless crooks had stolen the largest jewels that sunday
["DET", "ADJ", "NOUN", "VERB", "VERB", "DET", "ADJ", "NOUN", "ADP", "NOUN"],
["det", "amod", "nsubj", "aux", "root", "det", "amod", "dobj", "case", "nmod"],
[2, 1, 2, 1, 0, 2, 1, -3, 1, -5],
["De samvetslösa skurkarna", "de största juvelerna", "på söndagen"],
),
]
@pytest.mark.parametrize(
"text,pos,deps,heads,expected_noun_chunks", SV_NP_TEST_EXAMPLES
)
def test_sv_noun_chunks(sv_tokenizer, text, pos, deps, heads, expected_noun_chunks):
tokens = sv_tokenizer(text)
assert len(heads) == len(pos)
doc = get_doc(
tokens.vocab, words=[t.text for t in tokens], heads=heads, deps=deps, pos=pos
)
noun_chunks = list(doc.noun_chunks)
assert len(noun_chunks) == len(expected_noun_chunks)
for i, np in enumerate(noun_chunks):
assert np.text == expected_noun_chunks[i]