Python：正则表达式无法正常工作

2023年9月23日 150次阅读

我正在使用以下正则表达式,它假设找到字符串’U.S.A.’,但它只获得’A’,是否有人知道什么是错的？

#INPUT
import re

text = 'That U.S.A. poster-print costs $12.40...'

print re.findall(r'([A-Z]\.)+', text)

#OUTPUT
['A.']

预期产出：

['U.S.A.']

我正在关注NLTK Book,第3.7章here,它有一套正则表达式,但它只是不起作用.我在Python 2.7和3.4中都尝试过它.

>>> text = 'That U.S.A. poster-print costs $12.40...'
>>> pattern = r'''(?x)    # set flag to allow verbose regexps
...     ([A-Z]\.)+        # abbreviations, e.g. U.S.A.
...   | \w+(-\w+)*        # words with optional internal hyphens
...   | \$?\d+(\.\d+)?%?  # currency and percentages, e.g. $12.40, 82%
...   | \.\.\.            # ellipsis
...   | [][.,;"'?():-_`]  # these are separate tokens; includes ], [
... '''
>>> nltk.regexp_tokenize(text, pattern)
['That', 'U.S.A.', 'poster-print', 'costs', '$12.40', '...']

nltk.regexp_tokenize()与re.findall()的工作方式相同,我想我的python在某种程度上无法按预期识别正则表达式.上面列出的正则表达式输出：

[('', '', ''),
 ('A.', '', ''),
 ('', '-print', ''),
 ('', '', ''),
 ('', '', '.40'),
 ('', '', '')]

最佳答案 可能,这与先前使用在v3.1中废除的nltk.internals.compile_regexp_to_noncapturing()编译正则表达式有关,参见
here)

>>> import nltk
>>> nltk.__version__
'3.0.5'
>>> pattern = r'''(?x)               # set flag to allow verbose regexps
...               ([A-Z]\.)+         # abbreviations, e.g. U.S.A.
...               | \$?\d+(\.\d+)?%? # numbers, incl. currency and percentages
...               | \w+([-']\w+)*    # words w/ optional internal hyphens/apostrophe
...               | [+/\-@&*]        # special characters with meanings
...             '''
>>> 
>>> from nltk.tokenize.regexp import RegexpTokenizer
>>> tokeniser=RegexpTokenizer(pattern)
>>> line="My weight is about 68 kg, +/- 10 grams."
>>> tokeniser.tokenize(line)
['My', 'weight', 'is', 'about', '68', 'kg', '+', '/', '-', '10', 'grams']

但它在NLTK v3.1中不起作用：

>>> import nltk
>>> nltk.__version__
'3.1'
>>> pattern = r'''(?x)               # set flag to allow verbose regexps
...               ([A-Z]\.)+         # abbreviations, e.g. U.S.A.
...               | \$?\d+(\.\d+)?%? # numbers, incl. currency and percentages
...               | \w+([-']\w+)*    # words w/ optional internal hyphens/apostrophe
...               | [+/\-@&*]        # special characters with meanings
...             '''
>>> from nltk.tokenize.regexp import RegexpTokenizer
>>> tokeniser=RegexpTokenizer(pattern)
>>> line="My weight is about 68 kg, +/- 10 grams."
>>> tokeniser.tokenize(line)
[('', '', ''), ('', '', ''), ('', '', ''), ('', '', ''), ('', '', ''), ('', '', ''), ('', '', ''), ('', '', ''), ('', '', ''), ('', '', ''), ('', '', '')]

稍微修改一下你的正则表达式组的定义,你可以使用这个正则表达式在NLTK v3.1中使用相同的模式：

pattern = r"""(?x)                   # set flag to allow verbose regexps
              (?:[A-Z]\.)+           # abbreviations, e.g. U.S.A.
              |\d+(?:\.\d+)?%?       # numbers, incl. currency and percentages
              |\w+(?:[-']\w+)*       # words w/ optional internal hyphens/apostrophe
              |(?:[+/\-@&*])         # special characters with meanings
            """

在代码中：

>>> import nltk
>>> nltk.__version__
'3.1'
>>> pattern = r"""
... (?x)                   # set flag to allow verbose regexps
... (?:[A-Z]\.)+           # abbreviations, e.g. U.S.A.
... |\d+(?:\.\d+)?%?       # numbers, incl. currency and percentages
... |\w+(?:[-']\w+)*       # words w/ optional internal hyphens/apostrophe
... |(?:[+/\-@&*])         # special characters with meanings
... """
>>> from nltk.tokenize.regexp import RegexpTokenizer
>>> tokeniser=RegexpTokenizer(pattern)
>>> line="My weight is about 68 kg, +/- 10 grams."
>>> tokeniser.tokenize(line)
['My', 'weight', 'is', 'about', '68', 'kg', '+', '/', '-', '10', 'grams']

如果没有NLTK,使用python的re模块,我们发现本机不支持旧的正则表达式模式：

>>> pattern1 = r"""(?x)               # set flag to allow verbose regexps
...               ([A-Z]\.)+         # abbreviations, e.g. U.S.A.
...               |\$?\d+(\.\d+)?%? # numbers, incl. currency and percentages
...               |\w+([-']\w+)*    # words w/ optional internal hyphens/apostrophe
...               |[+/\-@&*]        # special characters with meanings
...               |\S\w*                       # any sequence of word characters# 
... """            
>>> text="My weight is about 68 kg, +/- 10 grams."
>>> re.findall(pattern1, text)
[('', '', ''), ('', '', ''), ('', '', ''), ('', '', ''), ('', '', ''), ('', '', ''), ('', '', ''), ('', '', ''), ('', '', ''), ('', '', ''), ('', '', ''), ('', '', ''), ('', '', '')]
>>> pattern2 = r"""(?x)                   # set flag to allow verbose regexps
...                       (?:[A-Z]\.)+           # abbreviations, e.g. U.S.A.
...                       |\d+(?:\.\d+)?%?       # numbers, incl. currency and percentages
...                       |\w+(?:[-']\w+)*       # words w/ optional internal hyphens/apostrophe
...                       |(?:[+/\-@&*])         # special characters with meanings
...                     """
>>> text="My weight is about 68 kg, +/- 10 grams."
>>> re.findall(pattern2, text)
['My', 'weight', 'is', 'about', '68', 'kg', '+', '/', '-', '10', 'grams']

注意：NLTK的RegexpTokenizer如何编译正则表达式的变化会使NLTK’s Regular Expression Tokenizer上的示例过时.

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