184 lines
7.8 KiB
Python
184 lines
7.8 KiB
Python
# -*- coding: utf-8 -*-
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# Natural Language Toolkit: Python port of the mteval-v14.pl tokenizer.
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#
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# Copyright (C) 2001-2015 NLTK Project
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# Author: Liling Tan (ported from ftp://jaguar.ncsl.nist.gov/mt/resources/mteval-v14.pl)
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# Contributors: Ozan Caglayan, Wiktor Stribizew
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#
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# URL: <http://nltk.sourceforge.net>
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# For license information, see LICENSE.TXT
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"""
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This is a NLTK port of the tokenizer used in the NIST BLEU evaluation script,
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https://github.com/moses-smt/mosesdecoder/blob/master/scripts/generic/mteval-v14.pl#L926
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which was also ported into Python in
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https://github.com/lium-lst/nmtpy/blob/master/nmtpy/metrics/mtevalbleu.py#L162
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"""
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from __future__ import unicode_literals
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import io
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import re
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from six import text_type
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from nltk.corpus import perluniprops
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from nltk.tokenize.api import TokenizerI
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from nltk.tokenize.util import xml_unescape
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class NISTTokenizer(TokenizerI):
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"""
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This NIST tokenizer is sentence-based instead of the original
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paragraph-based tokenization from mteval-14.pl; The sentence-based
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tokenization is consistent with the other tokenizers available in NLTK.
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>>> from six import text_type
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>>> from nltk.tokenize.nist import NISTTokenizer
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>>> nist = NISTTokenizer()
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>>> s = "Good muffins cost $3.88 in New York."
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>>> expected_lower = [u'good', u'muffins', u'cost', u'$', u'3.88', u'in', u'new', u'york', u'.']
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>>> expected_cased = [u'Good', u'muffins', u'cost', u'$', u'3.88', u'in', u'New', u'York', u'.']
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>>> nist.tokenize(s, lowercase=False) == expected_cased
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True
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>>> nist.tokenize(s, lowercase=True) == expected_lower # Lowercased.
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True
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The international_tokenize() is the preferred function when tokenizing
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non-european text, e.g.
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>>> from nltk.tokenize.nist import NISTTokenizer
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>>> nist = NISTTokenizer()
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# Input strings.
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>>> 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...'
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>>> amz = u'Amazon.com, Inc. (/ˈæməzɒn/) is an American electronic commerce...'
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>>> rkt = u'Rakuten, Inc. (楽天株式会社 Rakuten Kabushiki-gaisha) is a Japanese electronic commerce and Internet company based in Tokyo.'
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# Expected tokens.
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>>> 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')']
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>>> expected_amz = [u'Amazon', u'.', u'com', u',', u'Inc', u'.', u'(', u'/', u'\u02c8\xe6', u'm']
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>>> expected_rkt = [u'Rakuten', u',', u'Inc', u'.', u'(', u'\u697d\u5929\u682a\u5f0f\u4f1a\u793e', u'Rakuten', u'Kabushiki', u'-', u'gaisha']
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>>> nist.international_tokenize(albb)[:10] == expected_albb
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True
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>>> nist.international_tokenize(amz)[:10] == expected_amz
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True
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>>> nist.international_tokenize(rkt)[:10] == expected_rkt
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True
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# Doctest for patching issue #1926
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>>> sent = u'this is a foo\u2604sentence.'
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>>> expected_sent = [u'this', u'is', u'a', u'foo', u'\u2604', u'sentence', u'.']
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>>> nist.international_tokenize(sent) == expected_sent
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True
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"""
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# Strip "skipped" tags
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STRIP_SKIP = re.compile("<skipped>"), ""
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# Strip end-of-line hyphenation and join lines
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STRIP_EOL_HYPHEN = re.compile("\u2028"), " "
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# Tokenize punctuation.
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PUNCT = re.compile("([\{-\~\[-\` -\&\(-\+\:-\@\/])"), " \\1 "
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# Tokenize period and comma unless preceded by a digit.
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PERIOD_COMMA_PRECEED = re.compile("([^0-9])([\.,])"), "\\1 \\2 "
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# Tokenize period and comma unless followed by a digit.
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PERIOD_COMMA_FOLLOW = re.compile("([\.,])([^0-9])"), " \\1 \\2"
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# Tokenize dash when preceded by a digit
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DASH_PRECEED_DIGIT = re.compile("([0-9])(-)"), "\\1 \\2 "
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LANG_DEPENDENT_REGEXES = [
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PUNCT,
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PERIOD_COMMA_PRECEED,
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PERIOD_COMMA_FOLLOW,
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DASH_PRECEED_DIGIT,
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]
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# Perluniprops characters used in NIST tokenizer.
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pup_number = text_type("".join(set(perluniprops.chars("Number")))) # i.e. \p{N}
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pup_punct = text_type("".join(set(perluniprops.chars("Punctuation")))) # i.e. \p{P}
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pup_symbol = text_type("".join(set(perluniprops.chars("Symbol")))) # i.e. \p{S}
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# Python regexes needs to escape some special symbols, see
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# see https://stackoverflow.com/q/45670950/610569
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number_regex = re.sub(r"[]^\\-]", r"\\\g<0>", pup_number)
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punct_regex = re.sub(r"[]^\\-]", r"\\\g<0>", pup_punct)
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symbol_regex = re.sub(r"[]^\\-]", r"\\\g<0>", pup_symbol)
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# Note: In the original perl implementation, \p{Z} and \p{Zl} were used to
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# (i) strip trailing and heading spaces and
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# (ii) de-deuplicate spaces.
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# In Python, this would do: ' '.join(str.strip().split())
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# Thus, the next two lines were commented out.
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# Line_Separator = text_type(''.join(perluniprops.chars('Line_Separator'))) # i.e. \p{Zl}
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# Separator = text_type(''.join(perluniprops.chars('Separator'))) # i.e. \p{Z}
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# Pads non-ascii strings with space.
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NONASCII = re.compile("([\x00-\x7f]+)"), r" \1 "
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# Tokenize any punctuation unless followed AND preceded by a digit.
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PUNCT_1 = (
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re.compile("([{n}])([{p}])".format(n=number_regex, p=punct_regex)),
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"\\1 \\2 ",
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)
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PUNCT_2 = (
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re.compile("([{p}])([{n}])".format(n=number_regex, p=punct_regex)),
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" \\1 \\2",
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)
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# Tokenize symbols
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SYMBOLS = re.compile("([{s}])".format(s=symbol_regex)), " \\1 "
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INTERNATIONAL_REGEXES = [NONASCII, PUNCT_1, PUNCT_2, SYMBOLS]
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def lang_independent_sub(self, text):
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"""Performs the language independent string substituitions. """
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# It's a strange order of regexes.
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# It'll be better to unescape after STRIP_EOL_HYPHEN
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# but let's keep it close to the original NIST implementation.
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regexp, substitution = self.STRIP_SKIP
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text = regexp.sub(substitution, text)
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text = xml_unescape(text)
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regexp, substitution = self.STRIP_EOL_HYPHEN
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text = regexp.sub(substitution, text)
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return text
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def tokenize(self, text, lowercase=False, western_lang=True, return_str=False):
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text = text_type(text)
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# Language independent regex.
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text = self.lang_independent_sub(text)
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# Language dependent regex.
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if western_lang:
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# Pad string with whitespace.
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text = " " + text + " "
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if lowercase:
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text = text.lower()
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for regexp, substitution in self.LANG_DEPENDENT_REGEXES:
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text = regexp.sub(substitution, text)
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# Remove contiguous whitespaces.
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text = " ".join(text.split())
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# Finally, strips heading and trailing spaces
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# and converts output string into unicode.
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text = text_type(text.strip())
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return text if return_str else text.split()
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def international_tokenize(
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self, text, lowercase=False, split_non_ascii=True, return_str=False
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):
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text = text_type(text)
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# Different from the 'normal' tokenize(), STRIP_EOL_HYPHEN is applied
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# first before unescaping.
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regexp, substitution = self.STRIP_SKIP
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text = regexp.sub(substitution, text)
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regexp, substitution = self.STRIP_EOL_HYPHEN
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text = regexp.sub(substitution, text)
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text = xml_unescape(text)
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if lowercase:
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text = text.lower()
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for regexp, substitution in self.INTERNATIONAL_REGEXES:
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text = regexp.sub(substitution, text)
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# Make sure that there's only one space only between words.
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# Strip leading and trailing spaces.
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text = " ".join(text.strip().split())
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return text if return_str else text.split()
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