Initial commit: New language Luxembourgish (lb) (#4424)

* new language: Luxembourgish (lb)

* update

* update

* Update and rename .github/CONTRIBUTOR_AGREEMENT.md to .github/contributors/PeterGilles.md

* Update and rename .github/contributors/PeterGilles.md to .github/CONTRIBUTOR_AGREEMENT.md

* Update norm_exceptions.py

* Delete README.md

* moved test_lemma.py

* deactivated 'lemma_lookup = LOOKUP'

* update

* Update conftest.py

* update

* tests updated

* import unicode_literals

* Update spacy/tests/lang/lb/test_text.py

Co-Authored-By: Ines Montani <ines@ines.io>

* Create PeterGilles.md
This commit is contained in:
Peter Gilles 2019-10-14 12:27:50 +02:00 committed by Ines Montani
parent 98a961a60e
commit 428887b8f2
14 changed files with 608 additions and 1 deletions

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# spaCy contributor agreement
This spaCy Contributor Agreement (**"SCA"**) is based on the
[Oracle Contributor Agreement](http://www.oracle.com/technetwork/oca-405177.pdf).
The SCA applies to any contribution that you make to any product or project
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you grant to us in the contributed materials. The term **"us"** shall mean
[ExplosionAI GmbH](https://explosion.ai/legal). The term
**"you"** shall mean the person or entity identified below.
If you agree to be bound by these terms, fill in the information requested
below and include the filled-in version with your first pull request, under the
folder [`.github/contributors/`](/.github/contributors/). The name of the file
should be your GitHub username, with the extension `.md`. For example, the user
example_user would create the file `.github/contributors/example_user.md`.
Read this agreement carefully before signing. These terms and conditions
constitute a binding legal agreement.
## Contributor Agreement
1. The term "contribution" or "contributed materials" means any source code,
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* each contribution shall be in compliance with U.S. export control laws and
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* [X] I am signing on behalf of myself as an individual and no other person
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actual authority to contractually bind that entity.
## Contributor Details
| Field | Entry |
|------------------------------- | -------------------- |
| Name | Peter Gilles |
| Company name (if applicable) | |
| Title or role (if applicable) | |
| Date | 10.10. |
| GitHub username | Peter Gilles |
| Website (optional) | |

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spacy/lang/lb/__init__.py Normal file
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# coding: utf8
from __future__ import unicode_literals
from .tokenizer_exceptions import TOKENIZER_EXCEPTIONS
from .norm_exceptions import NORM_EXCEPTIONS
from .punctuation import TOKENIZER_INFIXES
from .lex_attrs import LEX_ATTRS
from .tag_map import TAG_MAP
from .stop_words import STOP_WORDS
#from .lemmatizer import LOOKUP
#from .syntax_iterators import SYNTAX_ITERATORS
from ..tokenizer_exceptions import BASE_EXCEPTIONS
from ..norm_exceptions import BASE_NORMS
from ...language import Language
from ...attrs import LANG, NORM
from ...util import update_exc, add_lookups
class LuxembourgishDefaults(Language.Defaults):
lex_attr_getters = dict(Language.Defaults.lex_attr_getters)
lex_attr_getters.update(LEX_ATTRS)
lex_attr_getters[LANG] = lambda text: 'lb'
lex_attr_getters[NORM] = add_lookups(Language.Defaults.lex_attr_getters[NORM], BASE_NORMS)
tokenizer_exceptions = update_exc(BASE_EXCEPTIONS, TOKENIZER_EXCEPTIONS)
stop_words = STOP_WORDS
#suffixes = TOKENIZER_SUFFIXES
#lemma_lookup = LOOKUP
class Luxembourgish(Language):
lang = 'lb'
Defaults = LuxembourgishDefaults
__all__ = ['Luxembourgish']

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# coding: utf8
from __future__ import unicode_literals
"""
Example sentences to test spaCy and its language models.
>>> from spacy.lang.lb.examples import sentences
>>> docs = nlp.pipe(sentences)
"""
sentences = [
"An der Zäit hunn sech den Nordwand an dSonn gestridden, wie vun hinnen zwee wuel méi staark wier, wéi e Wanderer, deen an ee waarme Mantel agepak war, iwwert de Wee koum.",
"Si goufen sech eens, dass deejéinege fir de Stäerkste gëlle sollt, deen de Wanderer forcéiere géif, säi Mantel auszedoen.",
"Den Nordwand huet mat aller Force geblosen, awer wat e méi geblosen huet, wat de Wanderer sech méi a säi Mantel agewéckelt huet.",
"Um Enn huet den Nordwand säi Kampf opginn.",
"Dunn huet dSonn dLoft mat hire frëndleche Strale gewiermt, a schonn no kuerzer Zäit huet de Wanderer säi Mantel ausgedoen.",
"Do huet den Nordwand missen zouginn, dass dSonn vun hinnen zwee de Stäerkste wier."
]

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# coding: utf8
from __future__ import unicode_literals
from ...attrs import LIKE_NUM
_num_words = set("""
null eent zwee dräi véier fënnef sechs ziwen aacht néng zéng eelef zwielef dräizéng
véierzéng foffzéng siechzéng siwwenzéng uechtzeng uechzeng nonnzéng nongzéng zwanzeg drësseg véierzeg foffzeg sechzeg siechzeg siwenzeg achtzeg achzeg uechtzeg uechzeg nonnzeg
honnert dausend millioun milliard billioun billiard trillioun triliard
""".split())
_ordinal_words = set("""
éischten zweeten drëtten véierten fënneften sechsten siwenten aachten néngten zéngten eeleften
zwieleften dräizéngten véierzéngten foffzéngten siechzéngten uechtzéngen uechzéngten nonnzéngten nongzéngten zwanzegsten
drëssegsten véierzegsten foffzegsten siechzegsten siwenzegsten uechzegsten nonnzegsten
honnertsten dausendsten milliounsten
milliardsten billiounsten billiardsten trilliounsten trilliardsten
""".split())
def like_num(text):
"""
check if text resembles a number
"""
text = text.replace(',', '').replace('.', '')
if text.isdigit():
return True
if text.count('/') == 1:
num, denom = text.split('/')
if num.isdigit() and denom.isdigit():
return True
if text in _num_words:
return True
if text in _ordinal_words:
return True
return False
LEX_ATTRS = {
LIKE_NUM: like_num
}

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# coding: utf8
from __future__ import unicode_literals
# TODO
# norm execptions: find a possibility to deal with the zillions of spelling variants (vläicht = vlaicht, vleicht, viläicht, viläischt, etc. etc.)
# here one could include the most common spelling mistakes
_exc = {
"datt": "dass",
"wgl.": "weg.",
"wgl.": "wegl.",
"vläicht": "viläicht"}
NORM_EXCEPTIONS = {}
for string, norm in _exc.items():
NORM_EXCEPTIONS[string] = norm
NORM_EXCEPTIONS[string.title()] = norm

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# coding: utf8
from __future__ import unicode_literals
from ..char_classes import LIST_ELLIPSES, LIST_ICONS
from ..char_classes import CONCAT_QUOTES, ALPHA, ALPHA_LOWER, ALPHA_UPPER
_quotes = CONCAT_QUOTES.replace("'", "")
_infixes = (
LIST_ELLIPSES
+ LIST_ICONS
+ [
r"(?<=[{al}])\.(?=[{au}])".format(al=ALPHA_LOWER, au=ALPHA_UPPER),
r"(?<=[{a}])[,!?](?=[{a}])".format(a=ALPHA),
r'(?<=[{a}])[:;<>=](?=[{a}])'.format(a=ALPHA),
r"(?<=[{a}]),(?=[{a}])".format(a=ALPHA),
r"(?<=[{a}])([{q}\)\]\(\[])(?=[{a}])".format(a=ALPHA, q=_quotes),
r"(?<=[{a}])--(?=[{a}])".format(a=ALPHA),
r"(?<=[0-9])-(?=[0-9])",
]
)
TOKENIZER_INFIXES = _infixes

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spacy/lang/lb/stop_words.py Normal file
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# coding: utf8
from __future__ import unicode_literals
STOP_WORDS = set("""
a
à
äis
är
ärt
äert
ären
all
allem
alles
alleguer
als
also
am
an
anerefalls
ass
aus
awer
bei
beim
bis
bis
d'
dach
datt
däin
där
dat
de
dee
den
deel
deem
deen
deene
déi
den
deng
denger
dem
der
dësem
di
dir
do
da
dann
domat
dozou
drop
du
duerch
duerno
e
ee
em
een
eent
ë
en
ënner
ëm
ech
eis
eise
eisen
eiser
eises
eisereen
esou
een
eng
enger
engem
entweder
et
eréischt
falls
fir
géint
géif
gëtt
gët
geet
gi
ginn
gouf
gouff
goung
hat
haten
hatt
hätt
hei
hu
huet
hun
hunn
hiren
hien
hin
hier
hir
jidderen
jiddereen
jiddwereen
jiddereng
jiddwerengen
jo
ins
iech
iwwer
kann
kee
keen
kënne
kënnt
kéng
kéngen
kéngem
koum
kuckt
mam
mat
ma
mech
méi
mécht
meng
menger
mer
mir
muss
nach
nämmlech
nämmelech
näischt
nawell
nëmme
nëmmen
net
nees
nee
no
nu
nom
och
oder
ons
onsen
onser
onsereen
onst
om
op
ouni
säi
säin
schonn
schonns
si
sid
sie
se
sech
seng
senge
sengem
senger
selwecht
selwer
sinn
sollten
souguer
sou
soss
sot
't
tëscht
u
un
um
virdrun
vu
vum
vun
wann
war
waren
was
wat
wëllt
weider
wéi
wéini
wéinst
wi
wollt
wou
wouhin
zanter
ze
zu
zum
zwar
""".split())

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spacy/lang/lb/tag_map.py Normal file
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# coding: utf8
from __future__ import unicode_literals
from ...symbols import POS, PUNCT, ADJ, CONJ, SCONJ, NUM, DET, ADV, ADP, X, VERB
from ...symbols import NOUN, PROPN, PART, INTJ, SPACE, PRON, AUX
# TODO: tag map is still using POS tags from an internal training set.
# These POS tags have to be modified to match those from Universal Dependencies
TAG_MAP = {
"$": {POS: PUNCT},
"ADJ": {POS: ADJ},
"AV": {POS: ADV},
"APPR": {POS: ADP, "AdpType": "prep"},
"APPRART": {POS: ADP, "AdpType": "prep", "PronType": "art"},
"D": {POS: DET, "PronType": "art"},
"KO": {POS: CONJ},
"N": {POS: NOUN},
"P": {POS: ADV},
"TRUNC": {POS: X, "Hyph": "yes"},
"AUX": {POS: AUX},
"V": {POS: VERB},
"MV": {POS: VERB, "VerbType": "mod"},
"PTK": {POS: PART},
"INTER": {POS: PART},
"NUM": {POS: NUM},
"_SP": {POS: SPACE},
}

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# coding: utf8
from __future__ import unicode_literals
from ...symbols import ORTH, LEMMA, TAG, NORM, PRON_LEMMA
from ..punctuation import TOKENIZER_PREFIXES
# TODO
# tokenize cliticised definite article "d'" as token of its own: d'Kanner > [d'] [Kanner]
# treat other apostrophes within words as part of the word: [op d'mannst], [fir d'éischt] (= exceptions)
# how to write the tokenisation exeption for the articles d' / D' ? This one is not working.
_prefixes = [prefix for prefix in TOKENIZER_PREFIXES if prefix not in ["d'", "D'", "d", "D", r"\' "]]
_exc = {
"d'mannst": [
{ORTH: "d'", LEMMA: "d'"},
{ORTH: "mannst", LEMMA: "mann", NORM: "mann"}],
"d'éischt": [
{ORTH: "d'", LEMMA: "d'"},
{ORTH: "éischt", LEMMA: "éischt", NORM: "éischt"}]
}
# translate / delete what is not necessary
# what does PRON_LEMMA mean?
for exc_data in [
{ORTH: "wgl.", LEMMA: "wann ech gelift", NORM: "wann ech gelieft"},
{ORTH: "M.", LEMMA: "Monsieur", NORM: "Monsieur"},
{ORTH: "Mme.", LEMMA: "Madame", NORM: "Madame"},
{ORTH: "Dr.", LEMMA: "Dokter", NORM: "Dokter"},
{ORTH: "Tel.", LEMMA: "Telefon", NORM: "Telefon"},
{ORTH: "asw.", LEMMA: "an sou weider", NORM: "an sou weider"},
{ORTH: "etc.", LEMMA: "et cetera", NORM: "et cetera"},
{ORTH: "bzw.", LEMMA: "bezéiungsweis", NORM: "bezéiungsweis"},
{ORTH: "Jan.", LEMMA: "Januar", NORM: "Januar"}]:
_exc[exc_data[ORTH]] = [exc_data]
# to be extended
for orth in [
"z.B.", "Dipl.", "Dr.", "etc.", "i.e.", "o.k.", "O.K.", "p.a.", "p.s.", "P.S.", "phil.",
"q.e.d.", "R.I.P.", "rer.", "sen.", "ë.a.", "U.S.", "U.S.A."]:
_exc[orth] = [{ORTH: orth}]
TOKENIZER_PREFIXES = _prefixes
TOKENIZER_EXCEPTIONS = _exc

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@ -134,7 +134,10 @@ def ko_tokenizer():
pytest.importorskip("natto")
return get_lang_class("ko").Defaults.create_tokenizer()
@pytest.fixture(scope="session")
def lb_tokenizer():
return get_lang_class("lb").Defaults.create_tokenizer()
@pytest.fixture(scope="session")
def lt_tokenizer():
return get_lang_class("lt").Defaults.create_tokenizer()

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# coding: utf-8
# from __future__ import unicolb_literals
from __future__ import unicode_literals
import pytest
@pytest.mark.parametrize("text", ["z.B.", "Jan."])
def test_lb_tokenizer_handles_abbr(lb_tokenizer, text):
tokens = lb_tokenizer(text)
assert len(tokens) == 1

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# coding: utf-8
#from __future__ import unicolb_literals
from __future__ import unicode_literals
import pytest
@pytest.mark.parametrize("text,length", [("z.B.", 1), ("zb.", 2), ("(z.B.", 2)])
def test_lb_tokenizer_splits_prefix_interact(lb_tokenizer, text, length):
tokens = lb_tokenizer(text)
assert len(tokens) == length
@pytest.mark.parametrize("text", ["z.B.)"])
def test_lb_tokenizer_splits_suffix_interact(lb_tokenizer, text):
tokens = lb_tokenizer(text)
assert len(tokens) == 2
@pytest.mark.parametrize("text", ["(z.B.)"])
def test_lb_tokenizer_splits_even_wrap_interact(lb_tokenizer, text):
tokens = lb_tokenizer(text)
assert len(tokens) == 3

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# coding: utf-8
from __future__ import unicode_literals
from __future__ import unicode_literals
import pytest
def test_lb_tokenizer_handles_long_text(lb_tokenizer):
text = """Den Nordwand an d'Sonn
An der Zäit hunn sech den Nordwand an dSonn gestridden, wie vun hinnen zwee wuel méi staark wier, wéi e Wanderer, deen an ee waarme Mantel agepak war, iwwert de Wee koum. Si goufen sech eens, dass deejéinege fir de Stäerkste gëlle sollt, deen de Wanderer forcéiere géif, säi Mantel auszedoen.",
Den Nordwand huet mat aller Force geblosen, awer wat e méi geblosen huet, wat de Wanderer sech méi a säi Mantel agewéckelt huet. Um Enn huet den Nordwand säi Kampf opginn.
Dunn huet dSonn dLoft mat hire frëndleche Strale gewiermt, a schonn no kuerzer Zäit huet de Wanderer säi Mantel ausgedoen.
Do huet den Nordwand missen zouginn, dass dSonn vun hinnen zwee de Stäerkste wier."""
tokens = lb_tokenizer(text)
assert len(tokens) == 143
@pytest.mark.parametrize(
"text,length",
[
("»Wat ass mat mir geschitt?«, huet hie geduecht.", 13),
("“Dëst fréi Opstoen”, denkt hien, “mécht ee ganz duercherneen. ", 15),
],
)
def test_lb_tokenizer_handles_examples(lb_tokenizer, text, length):
tokens = lb_tokenizer(text)
assert len(tokens) == length