173 lines
7.6 KiB
Python
173 lines
7.6 KiB
Python
# -*- coding: utf-8 -*-
|
||
# 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: <http://nltk.sourceforge.net>
|
||
# 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
|
||
"""
|
||
|
||
from __future__ import unicode_literals
|
||
|
||
import io
|
||
import re
|
||
from six import text_type
|
||
|
||
from nltk.corpus import perluniprops
|
||
from nltk.tokenize.api import TokenizerI
|
||
from nltk.tokenize.util import xml_unescape
|
||
|
||
|
||
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 six import text_type
|
||
>>> 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(u'\u2028'), ' '
|
||
# Tokenize punctuation.
|
||
PUNCT = re.compile('([\{-\~\[-\` -\&\(-\+\:-\@\/])'), ' \\1 '
|
||
# Tokenize period and comma unless preceded by a digit.
|
||
PERIOD_COMMA_PRECEED = re.compile('([^0-9])([\.,])'), '\\1 \\2 '
|
||
# Tokenize period and comma unless followed by a digit.
|
||
PERIOD_COMMA_FOLLOW = re.compile('([\.,])([^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 = text_type(''.join(set(perluniprops.chars('Number')))) # i.e. \p{N}
|
||
pup_punct = text_type(''.join(set(perluniprops.chars('Punctuation')))) # i.e. \p{P}
|
||
pup_symbol = text_type(''.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 = text_type(''.join(perluniprops.chars('Line_Separator'))) # i.e. \p{Zl}
|
||
#Separator = text_type(''.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(u"([{n}])([{p}])".format(n=number_regex, p=punct_regex)), '\\1 \\2 '
|
||
PUNCT_2 = re.compile(u"([{p}])([{n}])".format(n=number_regex, p=punct_regex)), ' \\1 \\2'
|
||
# Tokenize symbols
|
||
SYMBOLS = re.compile(u"([{s}])".format(s=symbol_regex)), ' \\1 '
|
||
|
||
INTERNATIONAL_REGEXES = [NONASCII, PUNCT_1, PUNCT_2, SYMBOLS]
|
||
|
||
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
|
||
|
||
def tokenize(self, text, lowercase=False,
|
||
western_lang=True, return_str=False):
|
||
text = text_type(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 = text_type(text.strip())
|
||
return text if return_str else text.split()
|
||
|
||
def international_tokenize(self, text, lowercase=False,
|
||
split_non_ascii=True,
|
||
return_str=False):
|
||
text = text_type(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()
|