# Natural Language Toolkit: Python port of the mteval-v14.pl tokenizer.
#
# Copyright (C) 2001-2015 NLTK Project
# Author: Liling Tan (ported from ftp://jaguar.ncsl.nist.gov/mt/resources/mteval-v14.pl)
# Contributors: Ozan Caglayan, Wiktor Stribizew
#
# URL: <https://www.nltk.org>
# For license information, see LICENSE.TXT
"""
This is a NLTK port of the tokenizer used in the NIST BLEU evaluation script,
https://github.com/moses-smt/mosesdecoder/blob/master/scripts/generic/mteval-v14.pl#L926
which was also ported into Python in
https://github.com/lium-lst/nmtpy/blob/master/nmtpy/metrics/mtevalbleu.py#L162
"""
import io
import re
from nltk.corpus import perluniprops
from nltk.tokenize.api import TokenizerI
from nltk.tokenize.util import xml_unescape
[docs]class NISTTokenizer(TokenizerI):
"""
This NIST tokenizer is sentence-based instead of the original
paragraph-based tokenization from mteval-14.pl; The sentence-based
tokenization is consistent with the other tokenizers available in NLTK.
>>> from nltk.tokenize.nist import NISTTokenizer
>>> nist = NISTTokenizer()
>>> s = "Good muffins cost $3.88 in New York."
>>> expected_lower = [u'good', u'muffins', u'cost', u'$', u'3.88', u'in', u'new', u'york', u'.']
>>> expected_cased = [u'Good', u'muffins', u'cost', u'$', u'3.88', u'in', u'New', u'York', u'.']
>>> nist.tokenize(s, lowercase=False) == expected_cased
True
>>> nist.tokenize(s, lowercase=True) == expected_lower # Lowercased.
True
The international_tokenize() is the preferred function when tokenizing
non-european text, e.g.
>>> from nltk.tokenize.nist import NISTTokenizer
>>> nist = NISTTokenizer()
# Input strings.
>>> albb = u'Alibaba Group Holding Limited (Chinese: 阿里巴巴集团控股 有限公司) us a Chinese e-commerce company...'
>>> amz = u'Amazon.com, Inc. (/ˈæməzɒn/) is an American electronic commerce...'
>>> rkt = u'Rakuten, Inc. (楽天株式会社 Rakuten Kabushiki-gaisha) is a Japanese electronic commerce and Internet company based in Tokyo.'
# Expected tokens.
>>> expected_albb = [u'Alibaba', u'Group', u'Holding', u'Limited', u'(', u'Chinese', u':', u'\u963f\u91cc\u5df4\u5df4\u96c6\u56e2\u63a7\u80a1', u'\u6709\u9650\u516c\u53f8', u')']
>>> expected_amz = [u'Amazon', u'.', u'com', u',', u'Inc', u'.', u'(', u'/', u'\u02c8\xe6', u'm']
>>> expected_rkt = [u'Rakuten', u',', u'Inc', u'.', u'(', u'\u697d\u5929\u682a\u5f0f\u4f1a\u793e', u'Rakuten', u'Kabushiki', u'-', u'gaisha']
>>> nist.international_tokenize(albb)[:10] == expected_albb
True
>>> nist.international_tokenize(amz)[:10] == expected_amz
True
>>> nist.international_tokenize(rkt)[:10] == expected_rkt
True
# Doctest for patching issue #1926
>>> sent = u'this is a foo\u2604sentence.'
>>> expected_sent = [u'this', u'is', u'a', u'foo', u'\u2604', u'sentence', u'.']
>>> nist.international_tokenize(sent) == expected_sent
True
"""
# Strip "skipped" tags
STRIP_SKIP = re.compile("<skipped>"), ""
# Strip end-of-line hyphenation and join lines
STRIP_EOL_HYPHEN = re.compile("\u2028"), " "
# Tokenize punctuation.
PUNCT = re.compile(r"([\{-\~\[-\` -\&\(-\+\:-\@\/])"), " \\1 "
# Tokenize period and comma unless preceded by a digit.
PERIOD_COMMA_PRECEED = re.compile(r"([^0-9])([\.,])"), "\\1 \\2 "
# Tokenize period and comma unless followed by a digit.
PERIOD_COMMA_FOLLOW = re.compile(r"([\.,])([^0-9])"), " \\1 \\2"
# Tokenize dash when preceded by a digit
DASH_PRECEED_DIGIT = re.compile("([0-9])(-)"), "\\1 \\2 "
LANG_DEPENDENT_REGEXES = [
PUNCT,
PERIOD_COMMA_PRECEED,
PERIOD_COMMA_FOLLOW,
DASH_PRECEED_DIGIT,
]
# Perluniprops characters used in NIST tokenizer.
pup_number = str("".join(set(perluniprops.chars("Number")))) # i.e. \p{N}
pup_punct = str("".join(set(perluniprops.chars("Punctuation")))) # i.e. \p{P}
pup_symbol = str("".join(set(perluniprops.chars("Symbol")))) # i.e. \p{S}
# Python regexes needs to escape some special symbols, see
# see https://stackoverflow.com/q/45670950/610569
number_regex = re.sub(r"[]^\\-]", r"\\\g<0>", pup_number)
punct_regex = re.sub(r"[]^\\-]", r"\\\g<0>", pup_punct)
symbol_regex = re.sub(r"[]^\\-]", r"\\\g<0>", pup_symbol)
# Note: In the original perl implementation, \p{Z} and \p{Zl} were used to
# (i) strip trailing and heading spaces and
# (ii) de-deuplicate spaces.
# In Python, this would do: ' '.join(str.strip().split())
# Thus, the next two lines were commented out.
# Line_Separator = str(''.join(perluniprops.chars('Line_Separator'))) # i.e. \p{Zl}
# Separator = str(''.join(perluniprops.chars('Separator'))) # i.e. \p{Z}
# Pads non-ascii strings with space.
NONASCII = re.compile("([\x00-\x7f]+)"), r" \1 "
# Tokenize any punctuation unless followed AND preceded by a digit.
PUNCT_1 = (
re.compile(f"([{number_regex}])([{punct_regex}])"),
"\\1 \\2 ",
)
PUNCT_2 = (
re.compile(f"([{punct_regex}])([{number_regex}])"),
" \\1 \\2",
)
# Tokenize symbols
SYMBOLS = re.compile(f"([{symbol_regex}])"), " \\1 "
INTERNATIONAL_REGEXES = [NONASCII, PUNCT_1, PUNCT_2, SYMBOLS]
[docs] def lang_independent_sub(self, text):
"""Performs the language independent string substituitions."""
# It's a strange order of regexes.
# It'll be better to unescape after STRIP_EOL_HYPHEN
# but let's keep it close to the original NIST implementation.
regexp, substitution = self.STRIP_SKIP
text = regexp.sub(substitution, text)
text = xml_unescape(text)
regexp, substitution = self.STRIP_EOL_HYPHEN
text = regexp.sub(substitution, text)
return text
[docs] def tokenize(self, text, lowercase=False, western_lang=True, return_str=False):
text = str(text)
# Language independent regex.
text = self.lang_independent_sub(text)
# Language dependent regex.
if western_lang:
# Pad string with whitespace.
text = " " + text + " "
if lowercase:
text = text.lower()
for regexp, substitution in self.LANG_DEPENDENT_REGEXES:
text = regexp.sub(substitution, text)
# Remove contiguous whitespaces.
text = " ".join(text.split())
# Finally, strips heading and trailing spaces
# and converts output string into unicode.
text = str(text.strip())
return text if return_str else text.split()
[docs] def international_tokenize(
self, text, lowercase=False, split_non_ascii=True, return_str=False
):
text = str(text)
# Different from the 'normal' tokenize(), STRIP_EOL_HYPHEN is applied
# first before unescaping.
regexp, substitution = self.STRIP_SKIP
text = regexp.sub(substitution, text)
regexp, substitution = self.STRIP_EOL_HYPHEN
text = regexp.sub(substitution, text)
text = xml_unescape(text)
if lowercase:
text = text.lower()
for regexp, substitution in self.INTERNATIONAL_REGEXES:
text = regexp.sub(substitution, text)
# Make sure that there's only one space only between words.
# Strip leading and trailing spaces.
text = " ".join(text.strip().split())
return text if return_str else text.split()