mirror of https://github.com/explosion/spaCy.git
93 lines
3.0 KiB
Python
93 lines
3.0 KiB
Python
# coding: utf8
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from __future__ import unicode_literals
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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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# fmt: on
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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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_num_words = [
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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",
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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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"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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"तीस",
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"चालीस",
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"पचास",
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"साठ",
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"सत्तर",
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"अस्सी",
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"नब्बे",
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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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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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break
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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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return False
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LEX_ATTRS = {NORM: norm, LIKE_NUM: like_num}
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