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ed7be3f64c
* Update norm_exceptions.py Extended the Currency set to include Franc, Indian Rupee, Bangladeshi Taka, Korean Won, Mexican Dollar, and Egyptian Pound * Fix formatting [ci skip]
64 lines
1.4 KiB
Python
64 lines
1.4 KiB
Python
# coding: utf8
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from __future__ import unicode_literals
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# These exceptions are used to add NORM values based on a token's ORTH value.
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# Individual languages can also add their own exceptions and overwrite them -
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# for example, British vs. American spelling in English.
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# Norms are only set if no alternative is provided in the tokenizer exceptions.
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# Note that this does not change any other token attributes. Its main purpose
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# is to normalise the word representations so that equivalent tokens receive
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# similar representations. For example: $ and € are very different, but they're
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# both currency symbols. By normalising currency symbols to $, all symbols are
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# seen as similar, no matter how common they are in the training data.
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BASE_NORMS = {
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"'s": "'s",
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"'S": "'s",
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"’s": "'s",
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"’S": "'s",
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"’": "'",
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"‘": "'",
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"´": "'",
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"`": "'",
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"”": '"',
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"“": '"',
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"''": '"',
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"``": '"',
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"´´": '"',
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"„": '"',
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"»": '"',
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"«": '"',
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"‘‘": '"',
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"’’": '"',
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"？": "?",
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"！": "!",
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"，": ",",
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"；": ";",
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"：": ":",
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"。": ".",
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"।": ".",
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"…": "...",
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"—": "-",
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"–": "-",
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"--": "-",
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"---": "-",
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"——": "-",
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"€": "$",
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"£": "$",
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"¥": "$",
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"฿": "$",
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"US$": "$",
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"C$": "$",
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"A$": "$",
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"₺": "$",
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"₹": "$",
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"৳": "$",
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"₩": "$",
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"Mex$": "$",
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"₣": "$",
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"E£": "$",
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}
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