Add Amharic አማርኛ Language support (#6583)

* Add Amharic to space

* clean up

* Add some PRON_LEMMA

* add Tigrinya support

* remove text_noun_chunks

* Tigrinya Support

* added some more details for ti

* fix unit test

* add amharic char range

* changes from review

* amharic and tigrinya share same unicode block

* get rid of _amharic/_tigrinya in char_classes

Co-authored-by: Josiah Solomon <jsolomon@meteorcomm.com>
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## Contributor Details
| Field | Entry |
|------------------------------- | -------------------- |
| Name | Josiah Solomon |
| Company name (if applicable) | |
| Title or role (if applicable) | |
| Date | 2020-12-15 |
| GitHub username | yosiasz |
| Website (optional) | |

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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 AmharicDefaults(Language.Defaults):
lex_attr_getters = dict(Language.Defaults.lex_attr_getters)
lex_attr_getters.update(LEX_ATTRS)
lex_attr_getters[LANG] = lambda text: "am"
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
writing_system = {"direction": "ltr", "has_case": False, "has_letters": True}
class Amharic(Language):
lang = "am"
Defaults = AmharicDefaults
__all__ = ["Amharic"]

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# coding: utf8
from __future__ import unicode_literals
"""
Example sentences to test spaCy and its language models.
>>> from spacy.lang.am.examples import sentences
>>> docs = nlp.pipe(sentences)
"""
sentences = [
"አፕል የዩኬን ጅምር ድርጅት በ 1 ቢሊዮን ዶላር ለመግዛት አስቧል።",
"የራስ ገዝ መኪኖች የኢንሹራንስ ኃላፊነትን ወደ አምራቾች ያዛውራሉ",
"ሳን ፍራንሲስኮ የእግረኛ መንገድ አቅርቦት ሮቦቶችን ማገድን ይመለከታል",
"ለንደን በእንግሊዝ የምትገኝ ትልቅ ከተማ ናት።",
"የት ነህ?",
"የፈረንሳይ ፕሬዝዳንት ማናቸው?",
"የአሜሪካ ዋና ከተማ ምንድነው?",
"ባራክ ኦባማ መቼ ተወለደ?",
]

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# coding: utf8
from __future__ import unicode_literals
from ...attrs import LIKE_NUM
_num_words = [
"ዜሮ",
"አንድ",
"ሁለት",
"ሶስት",
"አራት",
"አምስት",
"ስድስት",
"ሰባት",
"ስምት",
"ዘጠኝ",
"አስር",
"አስራ አንድ",
"አስራ ሁለት",
"አስራ ሶስት",
"አስራ አራት",
"አስራ አምስት",
"አስራ ስድስት",
"አስራ ሰባት",
"አስራ ስምንት",
"አስራ ዘጠኝ",
"ሃያ",
"ሰላሳ",
"አርባ",
"ሃምሳ",
"ስልሳ",
"ሰባ",
"ሰማንያ",
"ዘጠና",
"መቶ",
"ሺህ",
"ሚሊዮን",
"ቢሊዮን",
"ትሪሊዮን",
"ኳድሪሊዮን",
"ገጅሊዮን",
"ባዝሊዮን"
]
_ordinal_words = [
"አንደኛ",
"ሁለተኛ",
"ሶስተኛ",
"አራተኛ",
"አምስተኛ",
"ስድስተኛ",
"ሰባተኛ",
"ስምንተኛ",
"ዘጠነኛ",
"አስረኛ",
"አስራ አንደኛ",
"አስራ ሁለተኛ",
"አስራ ሶስተኛ",
"አስራ አራተኛ",
"አስራ አምስተኛ",
"አስራ ስድስተኛ",
"አስራ ሰባተኛ",
"አስራ ስምንተኛ",
"አስራ ዘጠነኛ",
"ሃያኛ",
"ሰላሳኛ"
"አርባኛ",
"አምሳኛ",
"ስድሳኛ",
"ሰባኛ",
"ሰማንያኛ",
"ዘጠናኛ",
"መቶኛ",
"ሺኛ",
"ሚሊዮንኛ",
"ቢሊዮንኛ",
"ትሪሊዮንኛ"
]
def like_num(text):
if text.startswith(("+", "-", "±", "~")):
text = text[1:]
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
text_lower = text.lower()
if text_lower in _num_words:
return True
# Check ordinal number
if text_lower in _ordinal_words:
return True
if text_lower.endswith(""):
if text_lower[:-2].isdigit():
return True
return False
LEX_ATTRS = {LIKE_NUM: like_num}

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# coding: utf8
from __future__ import unicode_literals
from ..char_classes import LIST_PUNCT, LIST_ELLIPSES, LIST_QUOTES, CURRENCY
from ..char_classes import UNITS, ALPHA_UPPER
_list_punct = LIST_PUNCT + "፡ ። ፣ ፤ ፥ ፦ ፧".strip().split()
_suffixes = (
_list_punct
+ LIST_ELLIPSES
+ LIST_QUOTES
+ [
r"(?<=[0-9])\+",
# Amharic is written from Left-To-Right
r"(?<=[0-9])(?:{c})".format(c=CURRENCY),
r"(?<=[0-9])(?:{u})".format(u=UNITS),
r"(?<=[{au}][{au}])\.".format(au=ALPHA_UPPER),
]
)
TOKENIZER_SUFFIXES = _suffixes

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# coding: utf8
from __future__ import unicode_literals
# Stop words
STOP_WORDS = set(
"""
ግን አንቺ አንተ እናንተ ያንተ ያንቺ የናንተ ራስህን ራስሽን ራሳችሁን
""".split()
)

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# coding: utf8
from __future__ import unicode_literals
from ...symbols import ORTH, LEMMA, NORM, PRON_LEMMA
_exc = {}
for exc_data in [
{ORTH: "ት/ቤት", LEMMA: "ትምህርት ቤት"},
{ORTH: "ወ/ሮ", LEMMA: PRON_LEMMA, NORM: "ወይዘሮ"},
]:
_exc[exc_data[ORTH]] = [exc_data]
for orth in [
"ዓ.ም.",
"ኪ.ሜ.",
]:
_exc[orth] = [{ORTH: orth}]
TOKENIZER_EXCEPTIONS = _exc

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@ -5,6 +5,8 @@ split_chars = lambda char: list(char.strip().split(" "))
merge_chars = lambda char: char.strip().replace(" ", "|") merge_chars = lambda char: char.strip().replace(" ", "|")
group_chars = lambda char: char.strip().replace(" ", "") group_chars = lambda char: char.strip().replace(" ", "")
_ethiopic = r"\u1200-\u137F"
_bengali = r"\u0980-\u09FF" _bengali = r"\u0980-\u09FF"
_hebrew = r"\u0591-\u05F4\uFB1D-\uFB4F" _hebrew = r"\u0591-\u05F4\uFB1D-\uFB4F"
@ -221,7 +223,8 @@ _upper = LATIN_UPPER + _russian_upper + _tatar_upper + _greek_upper + _ukrainian
_lower = LATIN_LOWER + _russian_lower + _tatar_lower + _greek_lower + _ukrainian_lower + _macedonian_lower _lower = LATIN_LOWER + _russian_lower + _tatar_lower + _greek_lower + _ukrainian_lower + _macedonian_lower
_uncased = ( _uncased = (
_bengali _ethiopic
+ _bengali
+ _hebrew + _hebrew
+ _persian + _persian
+ _sinhala + _sinhala

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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 TigrinyaDefaults(Language.Defaults):
lex_attr_getters = dict(Language.Defaults.lex_attr_getters)
lex_attr_getters.update(LEX_ATTRS)
lex_attr_getters[LANG] = lambda text: "ti"
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
writing_system = {"direction": "ltr", "has_case": False, "has_letters": True}
class Tigrinya(Language):
lang = "ti"
Defaults = TigrinyaDefaults
__all__ = ["Tigrinya"]

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# coding: utf8
from __future__ import unicode_literals
"""
Example sentences to test spaCy and its language models.
>>> from spacy.lang.ti.examples import sentences
>>> docs = nlp.pipe(sentences)
"""
sentences = [
"አፕል ብዩኬ ትርከብ ንግድ ብ1 ቢሊዮን ዶላር ንምግዛዕ ሐሲባ።",
"ፈላማይ ክታበት ኮቪድ 19 ተጀሚሩ፤ሓዱሽ ተስፋ ሂቡ ኣሎ",
"ቻንስለር ጀርመን ኣንገላ መርከል ዝርግሓ ቫይረስ ኮሮና ንምክልካል ጽኑዕ እገዳ ክግበር ጸዊዓ",
"ለንደን ብዓዲ እንግሊዝ ትርከብ ዓባይ ከተማ እያ።",
"ናበይ አለኻ፧",
"ናይ ፈረንሳይ ፕሬዝዳንት መን እዩ፧",
"ናይ አሜሪካ ዋና ከተማ እንታይ እያ፧",
"ኦባማ መዓስ ተወሊዱ፧",
]

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# coding: utf8
from __future__ import unicode_literals
from ...attrs import LIKE_NUM
_num_words = [
"ዜሮ",
"ሐደ",
"ክልተ",
"ሰለስተ",
"ኣርባዕተ",
"ሓሙሽተ",
"ሽድሽተ",
"ሸውዓተ",
"ሽሞንተ",
"ትሽዓተ",
"ኣሰርተ",
"ኣሰርተ ሐደ",
"ኣሰርተ ክልተ",
"ኣሰርተ ሰለስተ",
"ኣሰርተ ኣርባዕተ",
"ኣሰርተ ሓሙሽተ",
"ኣሰርተ ሽድሽተ",
"ኣሰርተ ሸውዓተ",
"ኣሰርተ ሽሞንተ",
"ኣሰርተ ትሽዓተ",
"ዕስራ",
"ሰላሳ",
"ኣርብዓ",
"ሃምሳ",
"ስልሳ",
"ሰብዓ",
"ሰማንያ",
"ተስዓ",
"ሚእቲ",
"ሺሕ",
"ሚልዮን",
"ቢልዮን",
"ትሪልዮን",
"ኳድሪልዮን",
"ገጅልዮን",
"ባዝልዮን"
]
_ordinal_words = [
"ቀዳማይ",
"ካልኣይ",
"ሳልሳይ",
"ራብኣይ",
"ሓምሻይ",
"ሻድሻይ",
"ሻውዓይ",
"ሻምናይ",
"ዘጠነኛ",
"አስረኛ",
"ኣሰርተ አንደኛ",
"ኣሰርተ ሁለተኛ",
"ኣሰርተ ሶስተኛ",
"ኣሰርተ አራተኛ",
"ኣሰርተ አምስተኛ",
"ኣሰርተ ስድስተኛ",
"ኣሰርተ ሰባተኛ",
"ኣሰርተ ስምንተኛ",
"ኣሰርተ ዘጠነኛ",
"ሃያኛ",
"ሰላሳኛ"
"አርባኛ",
"አምሳኛ",
"ስድሳኛ",
"ሰባኛ",
"ሰማንያኛ",
"ዘጠናኛ",
"መቶኛ",
"ሺኛ",
"ሚሊዮንኛ",
"ቢሊዮንኛ",
"ትሪሊዮንኛ"
]
def like_num(text):
if text.startswith(("+", "-", "±", "~")):
text = text[1:]
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
text_lower = text.lower()
if text_lower in _num_words:
return True
# Check ordinal number
if text_lower in _ordinal_words:
return True
if text_lower.endswith(""):
if text_lower[:-2].isdigit():
return True
return False
LEX_ATTRS = {LIKE_NUM: like_num}

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@ -0,0 +1,22 @@
# coding: utf8
from __future__ import unicode_literals
from ..char_classes import LIST_PUNCT, LIST_ELLIPSES, LIST_QUOTES, CURRENCY
from ..char_classes import UNITS, ALPHA_UPPER
_list_punct = LIST_PUNCT + "፡ ። ፣ ፤ ፥ ፦ ፧".strip().split()
_suffixes = (
_list_punct
+ LIST_ELLIPSES
+ LIST_QUOTES
+ [
r"(?<=[0-9])\+",
# Tigrinya is written from Left-To-Right
r"(?<=[0-9])(?:{c})".format(c=CURRENCY),
r"(?<=[0-9])(?:{u})".format(u=UNITS),
r"(?<=[{au}][{au}])\.".format(au=ALPHA_UPPER),
]
)
TOKENIZER_SUFFIXES = _suffixes

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@ -0,0 +1,10 @@
# coding: utf8
from __future__ import unicode_literals
# Stop words
STOP_WORDS = set(
"""
ግን ግና ንስኻ ንስኺ ንስኻትክን ንስኻትኩም ናትካ ናትኪ ናትክን ናትኩም
""".split()
)

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@ -0,0 +1,26 @@
# coding: utf8
from __future__ import unicode_literals
from ...symbols import ORTH, LEMMA, NORM, PRON_LEMMA
_exc = {}
for exc_data in [
{ORTH: "ት/ቤት", LEMMA: "ትምህርት ቤት"},
{ORTH: "ወ/ሮ", LEMMA: PRON_LEMMA, NORM: "ወይዘሮ"},
{ORTH: "ወ/ሪ", LEMMA: PRON_LEMMA, NORM: "ወይዘሪት"},
]:
_exc[exc_data[ORTH]] = [exc_data]
for orth in [
"ዓ.ም.",
"ኪ.ሜ.",
]:
_exc[orth] = [{ORTH: orth}]
TOKENIZER_EXCEPTIONS = _exc

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@ -31,6 +31,9 @@ def pytest_runtest_setup(item):
def tokenizer(): def tokenizer():
return get_lang_class("xx").Defaults.create_tokenizer() return get_lang_class("xx").Defaults.create_tokenizer()
@pytest.fixture(scope="session")
def am_tokenizer():
return get_lang_class("am").Defaults.create_tokenizer()
@pytest.fixture(scope="session") @pytest.fixture(scope="session")
def ar_tokenizer(): def ar_tokenizer():
@ -242,6 +245,9 @@ def th_tokenizer():
pytest.importorskip("pythainlp") pytest.importorskip("pythainlp")
return get_lang_class("th").Defaults.create_tokenizer() return get_lang_class("th").Defaults.create_tokenizer()
@pytest.fixture(scope="session")
def ti_tokenizer():
return get_lang_class("ti").Defaults.create_tokenizer()
@pytest.fixture(scope="session") @pytest.fixture(scope="session")
def tr_tokenizer(): def tr_tokenizer():

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@ -0,0 +1,55 @@
# coding: utf-8
from __future__ import unicode_literals
import pytest
from spacy.lang.am.lex_attrs import like_num
def test_am_tokenizer_handles_long_text(am_tokenizer):
text = """ሆሴ ሙጂካ በበጋ ወቅት በኦክስፎርድ ንግግር አንድያቀርቡ ሲጋበዙ ጭንቅላታቸው "ፈነዳ"
እጅግ ጥንታዊ የእንግሊዝኛ ተናጋሪ ዩኒቨርስቲ በአስር ሺዎች የሚቆጠሩ ዩሮዎችን ለተማሪዎች በማስተማር የሚያስከፍለው
እና ከማርጋሬት ታቸር እስከ ስቲቨን ሆኪንግ በአዳራሾቻቸው ውስጥ ንግግር ያደረጉበት የትምህርት ማዕከል በሞንቴቪዴኦ
በሚገኘው የመንግስት ትምህርት ቤት የሰለጠኑትን የ81 ዓመቱ አዛውንት አገልግሎት ጠየቁ"""
tokens = am_tokenizer(text)
assert len(tokens) == 56
@pytest.mark.parametrize(
"text,length",
[
("ሆሴ ሙጂካ ለምን ተመረጠ?", 5),
("“በፍፁም?”", 4),
("""አዎ! ሆዜ አርካዲዮ ቡንዲያ “እንሂድ” ሲል መለሰ።""", 11),
("እነሱ በግምት 10ኪ.ሜ. ሮጡ።", 7),
("እና ከዚያ ለምን...", 4),
],
)
def test_am_tokenizer_handles_cnts(am_tokenizer, text, length):
tokens = am_tokenizer(text)
assert len(tokens) == length
@pytest.mark.parametrize(
"text,match",
[
("10", True),
("1", True),
("10.000", True),
("1000", True),
("999,0", True),
("አንድ", True),
("ሁለት", True),
("ትሪሊዮን", True),
("ውሻ", False),
(",", False),
("1/2", True),
],
)
def test_lex_attrs_like_number(am_tokenizer, text, match):
tokens = am_tokenizer(text)
assert len(tokens) == 1
assert tokens[0].like_num == match

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@ -0,0 +1,55 @@
# coding: utf-8
from __future__ import unicode_literals
import pytest
from spacy.lang.ti.lex_attrs import like_num
def test_ti_tokenizer_handles_long_text(ti_tokenizer):
text = """ቻንስለር ጀርመን ኣንገላ መርከል ኣብታ ሃገር ቁጽሪ መትሓዝቲ ኮቪድ መዓልታዊ ክብረ መዝገብ ድሕሪ ምህራሙ- ጽኑዕ እገዳ ክግበር ጸዊዓ።
መርከል ሎሚ ንታሕታዋይ ባይቶ ሃገራ ክትገልጽ ከላ ኣብ ወሳኒ ምዕራፍ ቃልሲ ኢና ዘለና-ዳሕራዋይ ማዕበል ካብቲ ቀዳማይ ክገድድ ይኽእል` ኢላ
ትካል ምክልኻል ተላገብቲ ሕማማት ጀርመን ኣብ ዝሓለፈ 24 ሰዓታት ኣብ ምልእቲ ጀርመር 590 ሰባት ብኮቪድ19 ምሟቶም ኣፍሊጡ`
ቻንስለር ኣንጀላ መርከል ኣብ እዋን በዓላት ልደት ስድራቤታት ክተኣኻኸባ ዝፍቀደለን` እንተኾነ ድሕሪኡ ኣብ ዘሎ ግዜ ግን እቲ እገዳታት ክትግበር ትደሊ"""
tokens = ti_tokenizer(text)
assert len(tokens) == 85
@pytest.mark.parametrize(
"text,length",
[
("ቻንስለር ጀርመን ኣንገላ መርከል፧", 5),
("“ስድራቤታት፧”", 4),
("""ኣብ እዋን በዓላት ልደት ስድራቤታት ክተኣኻኸባ ዝፍቀደለን`ኳ እንተኾነ።""", 9),
("ብግምት 10ኪ.ሜ. ጎይዩ።", 6),
("ኣብ ዝሓለፈ 24 ሰዓታት...", 5),
],
)
def test_ti_tokenizer_handles_cnts(ti_tokenizer, text, length):
tokens = ti_tokenizer(text)
assert len(tokens) == length
@pytest.mark.parametrize(
"text,match",
[
("10", True),
("1", True),
("10.000", True),
("1000", True),
("999,0", True),
("ሐደ", True),
("ክልተ", True),
("ትሪልዮን", True),
("ከልቢ", False),
(",", False),
("1/2", True),
],
)
def test_lex_attrs_like_number(ti_tokenizer, text, match):
tokens = ti_tokenizer(text)
assert len(tokens) == 1
assert tokens[0].like_num == match