nltk.tokenize.TreebankWordTokenizer¶

class nltk.tokenize.TreebankWordTokenizer[source]¶

Bases: TokenizerI

The Treebank tokenizer uses regular expressions to tokenize text as in Penn Treebank.

This tokenizer performs the following steps:

split standard contractions, e.g. don't -> do n't and they'll -> they 'll
treat most punctuation characters as separate tokens
split off commas and single quotes, when followed by whitespace
separate periods that appear at the end of line

>>> from nltk.tokenize import TreebankWordTokenizer
>>> s = '''Good muffins cost $3.88\nin New York.  Please buy me\ntwo of them.\nThanks.'''
>>> TreebankWordTokenizer().tokenize(s)
['Good', 'muffins', 'cost', '$', '3.88', 'in', 'New', 'York.', 'Please', 'buy', 'me', 'two', 'of', 'them.', 'Thanks', '.']
>>> s = "They'll save and invest more."
>>> TreebankWordTokenizer().tokenize(s)
['They', "'ll", 'save', 'and', 'invest', 'more', '.']
>>> s = "hi, my name can't hello,"
>>> TreebankWordTokenizer().tokenize(s)
['hi', ',', 'my', 'name', 'ca', "n't", 'hello', ',']

STARTING_QUOTES = [(re.compile('^\\"'), '``'), (re.compile('(``)'), ' \\1 '), (re.compile('([ \\(\\[{<])(\\"|\\\'{2})'), '\\1 `` ')]¶

PUNCTUATION = [(re.compile('([:,])([^\\d])'), ' \\1 \\2'), (re.compile('([:,])$'), ' \\1 '), (re.compile('\\.\\.\\.'), ' ... '), (re.compile('[;@#$%&]'), ' \\g<0> '), (re.compile('([^\\.])(\\.)([\\]\\)}>"\\\']*)\\s*$'), '\\1 \\2\\3 '), (re.compile('[?!]'), ' \\g<0> '), (re.compile("([^'])' "), "\\1 ' ")]¶

PARENS_BRACKETS = (re.compile('[\\]\\[\$\$\\{\\}\\<\\>]'), ' \\g<0> ')¶

CONVERT_PARENTHESES = [(re.compile('\$'), '-LRB-'), (re.compile('\$'), '-RRB-'), (re.compile('\\['), '-LSB-'), (re.compile('\\]'), '-RSB-'), (re.compile('\\{'), '-LCB-'), (re.compile('\\}'), '-RCB-')]¶

DOUBLE_DASHES = (re.compile('--'), ' -- ')¶

ENDING_QUOTES = [(re.compile("''"), " '' "), (re.compile('"'), " '' "), (re.compile("([^' ])('[sS]|'[mM]|'[dD]|') "), '\\1 \\2 '), (re.compile("([^' ])('ll|'LL|'re|'RE|'ve|'VE|n't|N'T) "), '\\1 \\2 ')]¶

CONTRACTIONS2 = [re.compile('(?i)\\b(can)(?#X)(not)\\b', re.IGNORECASE), re.compile("(?i)\\b(d)(?#X)('ye)\\b", re.IGNORECASE), re.compile('(?i)\\b(gim)(?#X)(me)\\b', re.IGNORECASE), re.compile('(?i)\\b(gon)(?#X)(na)\\b', re.IGNORECASE), re.compile('(?i)\\b(got)(?#X)(ta)\\b', re.IGNORECASE), re.compile('(?i)\\b(lem)(?#X)(me)\\b', re.IGNORECASE), re.compile("(?i)\\b(more)(?#X)('n)\\b", re.IGNORECASE), re.compile('(?i)\\b(wan)(?#X)(na)(?=\\s)', re.IGNORECASE)]¶

CONTRACTIONS3 = [re.compile("(?i) ('t)(?#X)(is)\\b", re.IGNORECASE), re.compile("(?i) ('t)(?#X)(was)\\b", re.IGNORECASE)]¶

tokenize(text: str, convert_parentheses: bool = False, return_str: bool = False) → List[str][source]¶

Return a tokenized copy of text.

>>> from nltk.tokenize import TreebankWordTokenizer
>>> s = '''Good muffins cost $3.88 (roughly 3,36 euros)\nin New York.  Please buy me\ntwo of them.\nThanks.'''
>>> TreebankWordTokenizer().tokenize(s) 
['Good', 'muffins', 'cost', '$', '3.88', '(', 'roughly', '3,36',
'euros', ')', 'in', 'New', 'York.', 'Please', 'buy', 'me', 'two',
'of', 'them.', 'Thanks', '.']
>>> TreebankWordTokenizer().tokenize(s, convert_parentheses=True) 
['Good', 'muffins', 'cost', '$', '3.88', '-LRB-', 'roughly', '3,36',
'euros', '-RRB-', 'in', 'New', 'York.', 'Please', 'buy', 'me', 'two',
'of', 'them.', 'Thanks', '.']

Parameters

text (str) – A string with a sentence or sentences.
convert_parentheses (bool, optional) – if True, replace parentheses to PTB symbols, e.g. ( to -LRB-. Defaults to False.
return_str (bool, optional) – If True, return tokens as space-separated string, defaults to False.

Returns

List of tokens from text.

Return type

List[str]

span_tokenize(text: str) → Iterator[Tuple[int, int]][source]¶

Returns the spans of the tokens in text. Uses the post-hoc nltk.tokens.align_tokens to return the offset spans.

>>> from nltk.tokenize import TreebankWordTokenizer
>>> s = '''Good muffins cost $3.88\nin New (York).  Please (buy) me\ntwo of them.\n(Thanks).'''
>>> expected = [(0, 4), (5, 12), (13, 17), (18, 19), (19, 23),
... (24, 26), (27, 30), (31, 32), (32, 36), (36, 37), (37, 38),
... (40, 46), (47, 48), (48, 51), (51, 52), (53, 55), (56, 59),
... (60, 62), (63, 68), (69, 70), (70, 76), (76, 77), (77, 78)]
>>> list(TreebankWordTokenizer().span_tokenize(s)) == expected
True
>>> expected = ['Good', 'muffins', 'cost', '$', '3.88', 'in',
... 'New', '(', 'York', ')', '.', 'Please', '(', 'buy', ')',
... 'me', 'two', 'of', 'them.', '(', 'Thanks', ')', '.']
>>> [s[start:end] for start, end in TreebankWordTokenizer().span_tokenize(s)] == expected
True

Parameters: text (str) – A string with a sentence or sentences.
Yield: Tuple[int, int]
Return type: Iterator[Tuple[int, int]]

span_tokenize_sents(strings: List[str]) → Iterator[List[Tuple[int, int]]]¶

Apply self.span_tokenize() to each element of strings. I.e.:

return [self.span_tokenize(s) for s in strings]

Yield: List[Tuple[int, int]]
Parameters: strings (List[str]) –
Return type: Iterator[List[Tuple[int, int]]]

tokenize_sents(strings: List[str]) → List[List[str]]¶

Apply self.tokenize() to each element of strings. I.e.:

return [self.tokenize(s) for s in strings]

Return type: List[List[str]]
Parameters: strings (List[str]) –

NLTK

Documentation

nltk.tokenize.TreebankWordTokenizer¶