Hindi: Adds tests for lexical attributes (norm and like_num) (#5829)

* Hindi: Adds tests for lexical attributes (norm and like_num)

* Signs and sdds the contributor agreement

* Add ordinal numbers to be tagged as like_num

* Adds alternate pronunciation for 31 and 39
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5 changed files with 260 additions and 6 deletions

106
.github/contributors/rahul1990gupta.md vendored Normal file
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@ -0,0 +1,106 @@
# spaCy contributor agreement
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## Contributor Details
| Field | Entry |
|------------------------------- | -------------------- |
| Name | Rahul Gupta |
| Company name (if applicable) | |
| Title or role (if applicable) | |
| Date | 28 July 2020 |
| GitHub username | rahul1990gupta |
| Website (optional) | |

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@ -13,23 +13,26 @@ _stem_suffixes = [
["ाएगी", "ाएगा", "ाओगी", "ाओगे", "एंगी", "ेंगी", "एंगे", "ेंगे", "ूंगी", "ूंगा", "ातीं", "नाओं", "नाएं", "ताओं", "ताएं", "ियाँ", "ियों", "ियां"],
["ाएंगी", "ाएंगे", "ाऊंगी", "ाऊंगा", "ाइयाँ", "ाइयों", "ाइयां"]
]
# fmt: on
# reference 1:https://en.wikipedia.org/wiki/Indian_numbering_system
# reference 1: https://en.wikipedia.org/wiki/Indian_numbering_system
# reference 2: https://blogs.transparent.com/hindi/hindi-numbers-1-100/
# reference 3: https://www.mindurhindi.com/basic-words-and-phrases-in-hindi/
_num_words = [
_one_to_ten = [
"शून्य",
"एक",
"दो",
"तीन",
"चार",
"पांच",
"पांच", "पाँच",
"छह",
"सात",
"आठ",
"नौ",
"दस",
]
_eleven_to_beyond = [
"ग्यारह",
"बारह",
"तेरह",
@ -40,13 +43,85 @@ _num_words = [
"अठारह",
"उन्नीस",
"बीस",
"इकीस", "इक्कीस",
"बाईस",
"तेइस",
"चौबीस",
"पच्चीस",
"छब्बीस",
"सताइस", "सत्ताइस",
"अट्ठाइस",
"उनतीस",
"तीस",
"इकतीस", "इकत्तीस",
"बतीस", "बत्तीस",
"तैंतीस",
"चौंतीस",
"पैंतीस",
"छतीस", "छत्तीस",
"सैंतीस",
"अड़तीस",
"उनतालीस", "उनत्तीस",
"चालीस",
"इकतालीस",
"बयालीस",
"तैतालीस",
"चवालीस",
"पैंतालीस",
"छयालिस",
"सैंतालीस",
"अड़तालीस",
"उनचास",
"पचास",
"इक्यावन",
"बावन",
"तिरपन", "तिरेपन",
"चौवन", "चउवन",
"पचपन",
"छप्पन",
"सतावन", "सत्तावन",
"अठावन",
"उनसठ",
"साठ",
"इकसठ",
"बासठ",
"तिरसठ", "तिरेसठ",
"चौंसठ",
"पैंसठ",
"छियासठ",
"सड़सठ",
"अड़सठ",
"उनहत्तर",
"सत्तर",
"इकहत्तर"
"बहत्तर",
"तिहत्तर",
"चौहत्तर",
"पचहत्तर",
"छिहत्तर",
"सतहत्तर",
"अठहत्तर",
"उन्नासी", "उन्यासी"
"अस्सी",
"इक्यासी",
"बयासी",
"तिरासी",
"चौरासी",
"पचासी",
"छियासी",
"सतासी",
"अट्ठासी",
"नवासी",
"नब्बे",
"इक्यानवे",
"बानवे",
"तिरानवे",
"चौरानवे",
"पचानवे",
"छियानवे",
"सतानवे",
"अट्ठानवे",
"निन्यानवे",
"सौ",
"हज़ार",
"लाख",
@ -55,6 +130,22 @@ _num_words = [
"खरब",
]
_num_words = _one_to_ten + _eleven_to_beyond
_ordinal_words_one_to_ten = [
"प्रथम", "पहला",
"द्वितीय", "दूसरा",
"तृतीय", "तीसरा",
"चौथा",
"पांचवाँ",
"छठा",
"सातवाँ",
"आठवाँ",
"नौवाँ",
"दसवाँ",
]
_ordinal_suffix = "वाँ"
# fmt: on
def norm(string):
# normalise base exceptions, e.g. punctuation or currency symbols
@ -67,7 +158,7 @@ def norm(string):
for suffix_group in reversed(_stem_suffixes):
length = len(suffix_group[0])
if len(string) <= length:
break
continue
for suffix in suffix_group:
if string.endswith(suffix):
return string[:-length]
@ -77,7 +168,7 @@ def norm(string):
def like_num(text):
if text.startswith(("+", "-", "±", "~")):
text = text[1:]
text = text.replace(", ", "").replace(".", "")
text = text.replace(",", "").replace(".", "")
if text.isdigit():
return True
if text.count("/") == 1:
@ -86,6 +177,14 @@ def like_num(text):
return True
if text.lower() in _num_words:
return True
# check ordinal numbers
# reference: http://www.englishkitab.com/Vocabulary/Numbers.html
if text in _ordinal_words_one_to_ten:
return True
if text.endswith(_ordinal_suffix):
if text[:-len(_ordinal_suffix)] in _eleven_to_beyond:
return True
return False

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@ -123,6 +123,11 @@ def he_tokenizer():
return get_lang_class("he").Defaults.create_tokenizer()
@pytest.fixture(scope="session")
def hi_tokenizer():
return get_lang_class("hi").Defaults.create_tokenizer()
@pytest.fixture(scope="session")
def hr_tokenizer():
return get_lang_class("hr").Defaults.create_tokenizer()

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@ -0,0 +1,44 @@
# coding: utf-8
from __future__ import unicode_literals
import pytest
from spacy.lang.hi.lex_attrs import norm, like_num
def test_hi_tokenizer_handles_long_text(hi_tokenizer):
text = """
कह 1900 दशक शल (ि जयकर) पत चलत ि उसक
, वद (हर ) पस घर रह वद 10 पहल
पढ करन ि गय उसक टन शल अपन पड
रहन ि (िरण ) बत इस खबर
"""
tokens = hi_tokenizer(text)
assert len(tokens) == 86
@pytest.mark.parametrize(
"word,word_norm",
[
("चलता", "चल"),
("पढ़ाई", "पढ़"),
("देती", "दे"),
("जाती", ""),
("मुस्कुराकर", "मुस्कुर"),
],
)
def test_hi_norm(word, word_norm):
assert norm(word) == word_norm
@pytest.mark.parametrize(
"word", ["१९८७", "1987", "१२,२६७", "उन्नीस", "पाँच", "नवासी", "५/१०"],
)
def test_hi_like_num(word):
assert like_num(word)
@pytest.mark.parametrize(
"word", ["पहला", "तृतीय", "निन्यानवेवाँ", "उन्नीस", "तिहत्तरवाँ", "छत्तीसवाँ",],
)
def test_hi_like_num_ordinal_words(word):
assert like_num(word)