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* This comma has been most probably been left out unintentionally, leading to string concatenation between the two consecutive lines. This issue has been found automatically using a regular expression.
190 lines
5.8 KiB
Python
190 lines
5.8 KiB
Python
from ..norm_exceptions import BASE_NORMS
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from ...attrs import NORM, LIKE_NUM
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# fmt: off
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_stem_suffixes = [
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["ो", "े", "ू", "ु", "ी", "ि", "ा"],
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["कर", "ाओ", "िए", "ाई", "ाए", "ने", "नी", "ना", "ते", "ीं", "ती", "ता", "ाँ", "ां", "ों", "ें"],
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["ाकर", "ाइए", "ाईं", "ाया", "ेगी", "ेगा", "ोगी", "ोगे", "ाने", "ाना", "ाते", "ाती", "ाता", "तीं", "ाओं", "ाएं", "ुओं", "ुएं", "ुआं"],
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["ाएगी", "ाएगा", "ाओगी", "ाओगे", "एंगी", "ेंगी", "एंगे", "ेंगे", "ूंगी", "ूंगा", "ातीं", "नाओं", "नाएं", "ताओं", "ताएं", "ियाँ", "ियों", "ियां"],
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["ाएंगी", "ाएंगे", "ाऊंगी", "ाऊंगा", "ाइयाँ", "ाइयों", "ाइयां"]
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]
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# reference 1: https://en.wikipedia.org/wiki/Indian_numbering_system
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# reference 2: https://blogs.transparent.com/hindi/hindi-numbers-1-100/
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# reference 3: https://www.mindurhindi.com/basic-words-and-phrases-in-hindi/
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_one_to_ten = [
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"शून्य",
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"एक",
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"दो",
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"तीन",
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"चार",
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"पांच", "पाँच",
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"छह",
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"सात",
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"आठ",
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"नौ",
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"दस",
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]
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_eleven_to_beyond = [
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"ग्यारह",
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"बारह",
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"तेरह",
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"चौदह",
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"पंद्रह",
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"सोलह",
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"सत्रह",
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"अठारह",
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"उन्नीस",
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"बीस",
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"इकीस", "इक्कीस",
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"बाईस",
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"तेइस",
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"चौबीस",
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"पच्चीस",
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"छब्बीस",
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"सताइस", "सत्ताइस",
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"अट्ठाइस",
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"उनतीस",
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"तीस",
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"इकतीस", "इकत्तीस",
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"बतीस", "बत्तीस",
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"तैंतीस",
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"चौंतीस",
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"पैंतीस",
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"छतीस", "छत्तीस",
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"सैंतीस",
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"अड़तीस",
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"उनतालीस", "उनत्तीस",
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"चालीस",
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"इकतालीस",
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"बयालीस",
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"तैतालीस",
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"चवालीस",
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"पैंतालीस",
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"छयालिस",
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"सैंतालीस",
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"अड़तालीस",
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"उनचास",
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"पचास",
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"इक्यावन",
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"बावन",
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"तिरपन", "तिरेपन",
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"चौवन", "चउवन",
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"पचपन",
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"छप्पन",
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"सतावन", "सत्तावन",
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"अठावन",
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"उनसठ",
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"साठ",
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"इकसठ",
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"बासठ",
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"तिरसठ", "तिरेसठ",
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"चौंसठ",
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"पैंसठ",
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"छियासठ",
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"सड़सठ",
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"अड़सठ",
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"उनहत्तर",
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"सत्तर",
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"इकहत्तर",
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"बहत्तर",
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"तिहत्तर",
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"चौहत्तर",
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"पचहत्तर",
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"छिहत्तर",
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"सतहत्तर",
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"अठहत्तर",
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"उन्नासी", "उन्यासी"
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"अस्सी",
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"इक्यासी",
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"बयासी",
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"तिरासी",
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"चौरासी",
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"पचासी",
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"छियासी",
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"सतासी",
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"अट्ठासी",
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"नवासी",
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"नब्बे",
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"इक्यानवे",
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"बानवे",
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"तिरानवे",
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"चौरानवे",
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"पचानवे",
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"छियानवे",
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"सतानवे",
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"अट्ठानवे",
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"निन्यानवे",
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"सौ",
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"हज़ार",
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"लाख",
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"करोड़",
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"अरब",
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"खरब",
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]
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_num_words = _one_to_ten + _eleven_to_beyond
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_ordinal_words_one_to_ten = [
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"प्रथम", "पहला",
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"द्वितीय", "दूसरा",
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"तृतीय", "तीसरा",
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"चौथा",
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"पांचवाँ",
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"छठा",
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"सातवाँ",
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"आठवाँ",
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"नौवाँ",
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"दसवाँ",
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]
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_ordinal_suffix = "वाँ"
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# fmt: on
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def norm(string):
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# normalise base exceptions, e.g. punctuation or currency symbols
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if string in BASE_NORMS:
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return BASE_NORMS[string]
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# set stem word as norm, if available, adapted from:
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# http://computing.open.ac.uk/Sites/EACLSouthAsia/Papers/p6-Ramanathan.pdf
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# http://research.variancia.com/hindi_stemmer/
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# https://github.com/taranjeet/hindi-tokenizer/blob/master/HindiTokenizer.py#L142
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for suffix_group in reversed(_stem_suffixes):
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length = len(suffix_group[0])
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if len(string) <= length:
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continue
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for suffix in suffix_group:
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if string.endswith(suffix):
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return string[:-length]
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return string
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def like_num(text):
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if text.startswith(("+", "-", "±", "~")):
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text = text[1:]
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text = text.replace(",", "").replace(".", "")
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if text.isdigit():
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return True
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if text.count("/") == 1:
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num, denom = text.split("/")
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if num.isdigit() and denom.isdigit():
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return True
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if text.lower() in _num_words:
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return True
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# check ordinal numbers
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# reference: http://www.englishkitab.com/Vocabulary/Numbers.html
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if text in _ordinal_words_one_to_ten:
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return True
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if text.endswith(_ordinal_suffix):
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if text[: -len(_ordinal_suffix)] in _eleven_to_beyond:
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return True
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return False
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LEX_ATTRS = {NORM: norm, LIKE_NUM: like_num}
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