nltk.tokenize.regexp module

Regular-Expression Tokenizers

A RegexpTokenizer splits a string into substrings using a regular expression. For example, the following tokenizer forms tokens out of alphabetic sequences, money expressions, and any other non-whitespace sequences:

>>> from nltk.tokenize import RegexpTokenizer
>>> s = "Good muffins cost $3.88\nin New York.  Please buy me\ntwo of them.\n\nThanks."
>>> tokenizer = RegexpTokenizer(r'\w+|\$[\d\.]+|\S+')
>>> tokenizer.tokenize(s) 
['Good', 'muffins', 'cost', '$3.88', 'in', 'New', 'York', '.',
'Please', 'buy', 'me', 'two', 'of', 'them', '.', 'Thanks', '.']

A RegexpTokenizer can use its regexp to match delimiters instead:

>>> tokenizer = RegexpTokenizer(r'\s+', gaps=True)
>>> tokenizer.tokenize(s) 
['Good', 'muffins', 'cost', '$3.88', 'in', 'New', 'York.',
'Please', 'buy', 'me', 'two', 'of', 'them.', 'Thanks.']

Note that empty tokens are not returned when the delimiter appears at the start or end of the string.

The material between the tokens is discarded. For example, the following tokenizer selects just the capitalized words:

>>> capword_tokenizer = RegexpTokenizer(r'[A-Z]\w+')
>>> capword_tokenizer.tokenize(s)
['Good', 'New', 'York', 'Please', 'Thanks']

This module contains several subclasses of RegexpTokenizer that use pre-defined regular expressions.

>>> from nltk.tokenize import BlanklineTokenizer
>>> # Uses '\s*\n\s*\n\s*':
>>> BlanklineTokenizer().tokenize(s) 
['Good muffins cost $3.88\nin New York.  Please buy me\ntwo of them.',
'Thanks.']

All of the regular expression tokenizers are also available as functions:

>>> from nltk.tokenize import regexp_tokenize, wordpunct_tokenize, blankline_tokenize
>>> regexp_tokenize(s, pattern=r'\w+|\$[\d\.]+|\S+') 
['Good', 'muffins', 'cost', '$3.88', 'in', 'New', 'York', '.',
'Please', 'buy', 'me', 'two', 'of', 'them', '.', 'Thanks', '.']
>>> wordpunct_tokenize(s) 
['Good', 'muffins', 'cost', '$', '3', '.', '88', 'in', 'New', 'York',
 '.', 'Please', 'buy', 'me', 'two', 'of', 'them', '.', 'Thanks', '.']
>>> blankline_tokenize(s)
['Good muffins cost $3.88\nin New York.  Please buy me\ntwo of them.', 'Thanks.']

Caution: The function regexp_tokenize() takes the text as its first argument, and the regular expression pattern as its second argument. This differs from the conventions used by Python’s re functions, where the pattern is always the first argument. (This is for consistency with the other NLTK tokenizers.)

class nltk.tokenize.regexp.BlanklineTokenizer

Bases: RegexpTokenizer

Tokenize a string, treating any sequence of blank lines as a delimiter. Blank lines are defined as lines containing no characters, except for space or tab characters.

__init__()

class nltk.tokenize.regexp.RegexpTokenizer

Bases: TokenizerI

A tokenizer that splits a string using a regular expression, which matches either the tokens or the separators between tokens.

>>> tokenizer = RegexpTokenizer(r'\w+|\$[\d\.]+|\S+')

Parameters

• pattern (str) – The pattern used to build this tokenizer. (This pattern must not contain capturing parentheses; Use non-capturing parentheses, e.g. (?:…), instead)

• gaps (bool) – True if this tokenizer’s pattern should be used to find separators between tokens; False if this tokenizer’s pattern should be used to find the tokens themselves.

• discard_empty (bool) – True if any empty tokens ‘’ generated by the tokenizer should be discarded. Empty tokens can only be generated if _gaps == True.

• flags (int) – The regexp flags used to compile this tokenizer’s pattern. By default, the following flags are used: re.UNICODE | re.MULTILINE | re.DOTALL.

__init__(pattern, gaps=False, discard_empty=True, flags=RegexFlag.None)

span_tokenize(text)

Identify the tokens using integer offsets (start_i, end_i), where s[start_i:end_i] is the corresponding token.

Return type

Iterator[Tuple[int, int]]

tokenize(text)

Return a tokenized copy of s.

Return type

List[str]

class nltk.tokenize.regexp.WhitespaceTokenizer

Bases: RegexpTokenizer

Tokenize a string on whitespace (space, tab, newline). In general, users should use the string split() method instead.

>>> from nltk.tokenize import WhitespaceTokenizer
>>> s = "Good muffins cost $3.88\nin New York.  Please buy me\ntwo of them.\n\nThanks."
>>> WhitespaceTokenizer().tokenize(s) 
['Good', 'muffins', 'cost', '$3.88', 'in', 'New', 'York.',
'Please', 'buy', 'me', 'two', 'of', 'them.', 'Thanks.']

__init__()

class nltk.tokenize.regexp.WordPunctTokenizer

Bases: RegexpTokenizer

Tokenize a text into a sequence of alphabetic and non-alphabetic characters, using the regexp \w+|[^\w\s]+.

>>> from nltk.tokenize import WordPunctTokenizer
>>> s = "Good muffins cost $3.88\nin New York.  Please buy me\ntwo of them.\n\nThanks."
>>> WordPunctTokenizer().tokenize(s) 
['Good', 'muffins', 'cost', '$', '3', '.', '88', 'in', 'New', 'York',
'.', 'Please', 'buy', 'me', 'two', 'of', 'them', '.', 'Thanks', '.']

__init__()

nltk.tokenize.regexp.blankline_tokenize(text)

Return a tokenized copy of s.

Return type

List[str]

nltk.tokenize.regexp.regexp_tokenize(text, pattern, gaps=False, discard_empty=True, flags=RegexFlag.None)

Return a tokenized copy of text. See RegexpTokenizer for descriptions of the arguments.

nltk.tokenize.regexp.wordpunct_tokenize(text)

Return a tokenized copy of s.

Return type

List[str]