Add Arabic language (#2314)

* added support for Arabic lang

* added Arabic language support

* updated conftest
This commit is contained in:
Tahar Zanouda 2018-05-15 00:27:19 +02:00 committed by Matthew Honnibal
parent 0e08e49e87
commit 00417794d3
12 changed files with 595 additions and 11 deletions

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.github/contributors/tzano.md vendored Normal file
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## Contributor Details
| Field | Entry |
|------------------------------- | -------------------- |
| Name | Tahar Zanouda |
| Company name (if applicable) | |
| Title or role (if applicable) | |
| Date | 09-05-2018 |
| GitHub username | tzano |
| Website (optional) | |

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spacy/lang/ar/__init__.py Normal file
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# coding: utf8
from __future__ import unicode_literals
from .stop_words import STOP_WORDS
from .lex_attrs import LEX_ATTRS
from .punctuation import TOKENIZER_SUFFIXES
from .tokenizer_exceptions import TOKENIZER_EXCEPTIONS
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 ArabicDefaults(Language.Defaults):
lex_attr_getters = dict(Language.Defaults.lex_attr_getters)
lex_attr_getters.update(LEX_ATTRS)
lex_attr_getters[LANG] = lambda text: 'ar'
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
class Arabic(Language):
lang = 'ar'
Defaults = ArabicDefaults
__all__ = ['Arabic']

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spacy/lang/ar/examples.py Normal file
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# coding: utf8
from __future__ import unicode_literals
"""
Example sentences to test spaCy and its language models.
>>> from spacy.lang.ar.examples import sentences
>>> docs = nlp.pipe(sentences)
"""
sentences = [
"نال الكاتب خالد توفيق جائزة الرواية العربية في معرض الشارقة الدولي للكتاب",
"أين تقع دمشق ؟"
"كيف حالك ؟",
"هل يمكن ان نلتقي على الساعة الثانية عشرة ظهرا ؟",
"ماهي أبرز التطورات السياسية، الأمنية والاجتماعية في العالم ؟",
"هل بالإمكان أن نلتقي غدا؟",
"هناك نحو 382 مليون شخص مصاب بداء السكَّري في العالم",
"كشفت دراسة حديثة أن الخيل تقرأ تعبيرات الوجه وتستطيع أن تتذكر مشاعر الناس وعواطفهم"
]

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# coding: utf8
from __future__ import unicode_literals
from ...attrs import LIKE_NUM
_num_words = set("""
صفر
واحد
إثنان
اثنان
ثلاثة
ثلاثه
أربعة
أربعه
خمسة
خمسه
ستة
سته
سبعة
سبعه
ثمانية
ثمانيه
تسعة
تسعه
ﻋﺸﺮﺓ
ﻋﺸﺮه
عشرون
عشرين
ثلاثون
ثلاثين
اربعون
اربعين
أربعون
أربعين
خمسون
خمسين
ستون
ستين
سبعون
سبعين
ثمانون
ثمانين
تسعون
تسعين
مائتين
مائتان
ثلاثمائة
خمسمائة
سبعمائة
الف
آلاف
ملايين
مليون
مليار
مليارات
""".split())
_ordinal_words = set("""
اول
أول
حاد
واحد
ثان
ثاني
ثالث
رابع
خامس
سادس
سابع
ثامن
تاسع
عاشر
""".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
from ..punctuation import TOKENIZER_INFIXES
from ..char_classes import LIST_PUNCT, LIST_ELLIPSES, LIST_QUOTES, CURRENCY
from ..char_classes import QUOTES, UNITS, ALPHA, ALPHA_LOWER, ALPHA_UPPER
_suffixes = (LIST_PUNCT + LIST_ELLIPSES + LIST_QUOTES +
[r'(?<=[0-9])\+',
# Arabic is written from Right-To-Left
r'(?<=[0-9])(?:{})'.format(CURRENCY),
r'(?<=[0-9])(?:{})'.format(UNITS),
r'(?<=[{au}][{au}])\.'.format(au=ALPHA_UPPER)])
TOKENIZER_SUFFIXES = _suffixes

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spacy/lang/ar/stop_words.py Normal file
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# coding: utf8
from __future__ import unicode_literals
STOP_WORDS = set("""
من
نحو
لعل
بما
بين
وبين
ايضا
وبينما
تحت
مثلا
لدي
عنه
مع
هي
وهذا
واذا
هذان
انه
بينما
أمسى
وسوف
ولم
لذلك
إلى
منه
منها
كما
ظل
هنا
به
كذلك
اما
هما
بعد
بينهم
التي
أبو
اذا
بدلا
لها
أمام
يلي
حين
ضد
الذي
قد
صار
إذا
مابرح
قبل
كل
وليست
الذين
لهذا
وثي
انهم
باللتي
مافتئ
ولا
بهذه
بحيث
كيف
وله
علي
بات
لاسيما
حتى
وقد
و
أما
فيها
بهذا
لذا
حيث
لقد
إن
فإن
اول
ليت
فاللتي
ولقد
لسوف
هذه
ولماذا
معه
الحالي
بإن
حول
في
عليه
مايزال
ولعل
أنه
أضحى
اي
ستكون
لن
أن
ضمن
وعلى
امسى
الي
ذات
ولايزال
ذلك
فقد
هم
أي
عند
ابن
أو
فهو
فانه
سوف
ما
آل
كلا
عنها
وكذلك
ليست
لم
وأن
ماذا
لو
وهل
اللتي
ولذا
يمكن
فيه
الا
عليها
وبينهم
يوم
وبما
لما
فكان
اضحى
اصبح
لهم
بها
او
الذى
الى
إلي
قال
والتي
لازال
أصبح
ولهذا
مثل
وكانت
لكنه
بذلك
هذا
لماذا
قالت
فقط
لكن
مما
وكل
وان
وأبو
ومن
كان
مازال
هل
بينهن
هو
وما
على
وهو
لأن
واللتي
والذي
دون
عن
وايضا
هناك
بلا
جدا
ثم
منذ
اللذين
لايزال
بعض
مساء
تكون
فلا
بيننا
لا
ولكن
إذ
وأثناء
ليس
ومع
فيهم
ولسوف
بل
تلك
أحد
وهي
وكان
ومنها
وفي
ماانفك
اليوم
وماذا
هؤلاء
وليس
له
أثناء
بد
اليه
كأن
اليها
بتلك
يكون
ولما
هن
والى
كانت
وقبل
ان
لدى
""".split())

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# coding: utf8
from __future__ import unicode_literals
from ...symbols import ORTH, LEMMA, TAG, NORM, PRON_LEMMA
import re
_exc = {}
# time
for exc_data in [
{LEMMA: "قبل الميلاد", ORTH: "ق.م"},
{LEMMA: "بعد الميلاد", ORTH: "ب. م"},
{LEMMA: "ميلادي", ORTH: ""},
{LEMMA: "هجري", ORTH: ".هـ"},
{LEMMA: "توفي", ORTH: ""}]:
_exc[exc_data[ORTH]] = [exc_data]
# scientific abv.
for exc_data in [
{LEMMA: "صلى الله عليه وسلم", ORTH: "صلعم"},
{LEMMA: "الشارح", ORTH: "الشـ"},
{LEMMA: "الظاهر", ORTH: "الظـ"},
{LEMMA: "أيضًا", ORTH: "أيضـ"},
{LEMMA: "إلى آخره", ORTH: "إلخ"},
{LEMMA: "انتهى", ORTH: "اهـ"},
{LEMMA: "حدّثنا", ORTH: "ثنا"},
{LEMMA: "حدثني", ORTH: "ثنى"},
{LEMMA: "أنبأنا", ORTH: "أنا"},
{LEMMA: "أخبرنا", ORTH: "نا"},
{LEMMA: "مصدر سابق", ORTH: "م. س"},
{LEMMA: "مصدر نفسه", ORTH: "م. ن"}]:
_exc[exc_data[ORTH]] = [exc_data]
# other abv.
for exc_data in [
{LEMMA: "دكتور", ORTH: "د."},
{LEMMA: "أستاذ دكتور", ORTH: "أ.د"},
{LEMMA: "أستاذ", ORTH: "أ."},
{LEMMA: "بروفيسور", ORTH: "ب."}]:
_exc[exc_data[ORTH]] = [exc_data]
for exc_data in [
{LEMMA: "تلفون", ORTH: "ت."},
{LEMMA: "صندوق بريد", ORTH: "ص.ب"}]:
_exc[exc_data[ORTH]] = [exc_data]
TOKENIZER_EXCEPTIONS = _exc

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@ -3,13 +3,11 @@ from __future__ import unicode_literals
import regex as re
re.DEFAULT_VERSION = re.VERSION1
merge_char_classes = lambda classes: '[{}]'.format('||'.join(classes))
split_chars = lambda char: list(char.strip().split(' '))
merge_chars = lambda char: char.strip().replace(' ', '|')
_bengali = r'[\p{L}&&\p{Bengali}]'
_hebrew = r'[\p{L}&&\p{Hebrew}]'
_latin_lower = r'[\p{Ll}&&\p{Latin}]'
@ -27,11 +25,11 @@ ALPHA = merge_char_classes(_upper + _lower + _uncased)
ALPHA_LOWER = merge_char_classes(_lower + _uncased)
ALPHA_UPPER = merge_char_classes(_upper + _uncased)
_units = ('km km² km³ m m² m³ dm dm² dm³ cm cm² cm³ mm mm² mm³ ha µm nm yd in ft '
'kg g mg µg t lb oz m/s km/h kmh mph hPa Pa mbar mb MB kb KB gb GB tb '
'TB T G M K % км км² км³ м м² м³ дм дм² дм³ см см² см³ мм мм² мм³ нм '
'кг г мг м/с км/ч кПа Па мбар Кб КБ кб Мб МБ мб Гб ГБ гб Тб ТБ тб')
'кг г мг м/с км/ч кПа Па мбар Кб КБ кб Мб МБ мб Гб ГБ гб Тб ТБ тб'
'كم كم² كم³ م م² م³ سم سم² سم³ مم مم² مم³ كم غرام جرام جم كغ ملغ كوب اكواب')
_currency = r'\$ £ € ¥ ฿ US\$ C\$ A\$ ₽ ﷼'
# These expressions contain various unicode variations, including characters
@ -45,7 +43,6 @@ _hyphens = '- — -- --- —— ~'
# Details: https://www.compart.com/en/unicode/category/So
_other_symbols = r'[\p{So}]'
UNITS = merge_chars(_units)
CURRENCY = merge_chars(_currency)
QUOTES = merge_chars(_quotes)

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@ -15,7 +15,9 @@ from .. import util
# here if it's using spaCy's tokenizer (not a different library)
# TODO: re-implement generic tokenizer tests
_languages = ['bn', 'da', 'de', 'en', 'es', 'fi', 'fr', 'ga', 'he', 'hu', 'id',
'it', 'nb', 'nl', 'pl', 'pt', 'ru', 'sv', 'tr', 'ar', 'xx']
'it', 'nb', 'nl', 'pl', 'pt', 'ro', 'ru', 'sv', 'tr', 'xx']
_models = {'en': ['en_core_web_sm'],
'de': ['de_core_news_md'],
'fr': ['fr_core_news_sm'],
@ -152,6 +154,9 @@ def th_tokenizer():
def tr_tokenizer():
return util.get_lang_class('tr').Defaults.create_tokenizer()
@pytest.fixture
def ar_tokenizer():
return util.get_lang_class('ar').Defaults.create_tokenizer()
@pytest.fixture
def ru_tokenizer():

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@ -0,0 +1,26 @@
# coding: utf-8
from __future__ import unicode_literals
import pytest
@pytest.mark.parametrize('text',
["ق.م", "إلخ", "ص.ب", "ت."])
def test_ar_tokenizer_handles_abbr(ar_tokenizer, text):
tokens = ar_tokenizer(text)
assert len(tokens) == 1
def test_ar_tokenizer_handles_exc_in_text(ar_tokenizer):
text = u"تعود الكتابة الهيروغليفية إلى سنة 3200 ق.م"
tokens = ar_tokenizer(text)
assert len(tokens) == 7
assert tokens[6].text == "ق.م"
assert tokens[6].lemma_ == "قبل الميلاد"
def test_ar_tokenizer_handles_exc_in_text(ar_tokenizer):
text = u"يبلغ طول مضيق طارق 14كم "
tokens = ar_tokenizer(text)
print([(tokens[i].text, tokens[i].suffix_) for i in range(len(tokens))])
assert len(tokens) == 6

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@ -0,0 +1,13 @@
# coding: utf8
from __future__ import unicode_literals
def test_tokenizer_handles_long_text(ar_tokenizer):
text = """نجيب محفوظ مؤلف و كاتب روائي عربي، يعد من أهم الأدباء العرب خلال القرن العشرين.
ولد نجيب محفوظ في مدينة القاهرة، حيث ترعرع و تلقى تعليمه الجامعي في جامعتها،
فتمكن من نيل شهادة في الفلسفة. ألف محفوظ على مدار حياته الكثير من الأعمال الأدبية، و في مقدمتها ثلاثيته الشهيرة.
و قد نجح في الحصول على جائزة نوبل للآداب، ليكون بذلك العربي الوحيد الذي فاز بها."""
tokens = ar_tokenizer(text)
assert tokens[3].is_stop == True
assert len(tokens) == 77