Source code for nltk.tokenize.treebank

# Natural Language Toolkit: Tokenizers
#
# Copyright (C) 2001-2017 NLTK Project
# Author: Edward Loper <edloper@gmail.com>
#         Michael Heilman <mheilman@cmu.edu> (re-port from http://www.cis.upenn.edu/~treebank/tokenizer.sed)
#
# URL: <http://nltk.sourceforge.net>
# For license information, see LICENSE.TXT

r"""

Penn Treebank Tokenizer

The Treebank tokenizer uses regular expressions to tokenize text as in Penn Treebank.
This implementation is a port of the tokenizer sed script written by Robert McIntyre
and available at http://www.cis.upenn.edu/~treebank/tokenizer.sed.
"""

import re
from nltk.tokenize.api import TokenizerI
from nltk.tokenize.util import align_tokens


[docs]class MacIntyreContractions: """ List of contractions adapted from Robert MacIntyre's tokenizer. """ CONTRACTIONS2 = [r"(?i)\b(can)(?#X)(not)\b", r"(?i)\b(d)(?#X)('ye)\b", r"(?i)\b(gim)(?#X)(me)\b", r"(?i)\b(gon)(?#X)(na)\b", r"(?i)\b(got)(?#X)(ta)\b", r"(?i)\b(lem)(?#X)(me)\b", r"(?i)\b(mor)(?#X)('n)\b", r"(?i)\b(wan)(?#X)(na)\s"] CONTRACTIONS3 = [r"(?i) ('t)(?#X)(is)\b", r"(?i) ('t)(?#X)(was)\b"] CONTRACTIONS4 = [r"(?i)\b(whad)(dd)(ya)\b", r"(?i)\b(wha)(t)(cha)\b"]
[docs]class TreebankWordTokenizer(TokenizerI): """ The Treebank tokenizer uses regular expressions to tokenize text as in Penn Treebank. This is the method that is invoked by ``word_tokenize()``. It assumes that the text has already been segmented into sentences, e.g. using ``sent_tokenize()``. 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 STARTING_QUOTES = [ (re.compile(r'^\"'), r'``'), (re.compile(r'(``)'), r' \1 '), (re.compile(r'([ (\[{<])"'), r'\1 `` '), ] #punctuation PUNCTUATION = [ (re.compile(r'([:,])([^\d])'), r' \1 \2'), (re.compile(r'([:,])$'), r' \1 '), (re.compile(r'\.\.\.'), r' ... '), (re.compile(r'[;@#$%&]'), r' \g<0> '), (re.compile(r'([^\.])(\.)([\]\)}>"\']*)\s*$'), r'\1 \2\3 '), # Handles the final period. (re.compile(r'[?!]'), r' \g<0> '), (re.compile(r"([^'])' "), r"\1 ' "), ] # Pads parentheses PARENS_BRACKETS = (re.compile(r'[\]\[\(\)\{\}\<\>]'), r' \g<0> ') # Optionally: Convert parentheses, brackets and converts them to PTB symbols. CONVERT_PARENTHESES = [ (re.compile(r'\('), '-LRB-'), (re.compile(r'\)'), '-RRB-'), (re.compile(r'\['), '-LSB-'), (re.compile(r'\]'), '-RSB-'), (re.compile(r'\{'), '-LCB-'), (re.compile(r'\}'), '-RCB-') ] DOUBLE_DASHES = (re.compile(r'--'), r' -- ') #ending quotes ENDING_QUOTES = [ (re.compile(r'"'), " '' "), (re.compile(r'(\S)(\'\')'), r'\1 \2 '), (re.compile(r"([^' ])('[sS]|'[mM]|'[dD]|') "), r"\1 \2 "), (re.compile(r"([^' ])('ll|'LL|'re|'RE|'ve|'VE|n't|N'T) "), r"\1 \2 "), ] # List of contractions adapted from Robert MacIntyre's tokenizer. _contractions = MacIntyreContractions() CONTRACTIONS2 = list(map(re.compile, _contractions.CONTRACTIONS2)) CONTRACTIONS3 = list(map(re.compile, _contractions.CONTRACTIONS3))
[docs] def tokenize(self, text, convert_parentheses=False, return_str=False): for regexp, substitution in self.STARTING_QUOTES: text = regexp.sub(substitution, text) for regexp, substitution in self.PUNCTUATION: text = regexp.sub(substitution, text) # Handles parentheses. regexp, substitution = self.PARENS_BRACKETS text = regexp.sub(substitution, text) # Optionally convert parentheses if convert_parentheses: for regexp, substitution in self.CONVERT_PARENTHESES: text = regexp.sub(substitution, text) # Handles double dash. regexp, substitution = self.DOUBLE_DASHES text = regexp.sub(substitution, text) #add extra space to make things easier text = " " + text + " " for regexp, substitution in self.ENDING_QUOTES: text = regexp.sub(substitution, text) for regexp in self.CONTRACTIONS2: text = regexp.sub(r' \1 \2 ', text) for regexp in self.CONTRACTIONS3: text = regexp.sub(r' \1 \2 ', text) # We are not using CONTRACTIONS4 since # they are also commented out in the SED scripts # for regexp in self._contractions.CONTRACTIONS4: # text = regexp.sub(r' \1 \2 \3 ', text) return text if return_str else text.split()
[docs] def span_tokenize(self, text): """ Uses the post-hoc nltk.tokens.align_tokens to return the offset spans. >>> from nltk.tokenize import TreebankWordTokenizer >>> s = '''Good muffins cost $3.88\\nin New (York). Please (buy) me\\ntwo of them.\\n(Thanks).''' >>> expected = [(0, 4), (5, 12), (13, 17), (18, 19), (19, 23), ... (24, 26), (27, 30), (31, 32), (32, 36), (36, 37), (37, 38), ... (40, 46), (47, 48), (48, 51), (51, 52), (53, 55), (56, 59), ... (60, 62), (63, 68), (69, 70), (70, 76), (76, 77), (77, 78)] >>> TreebankWordTokenizer().span_tokenize(s) == expected True >>> expected = ['Good', 'muffins', 'cost', '$', '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 """ raw_tokens = self.tokenize(text) # Convert converted quotes back to original double quotes # Do this only if original text contains double quote(s) if '"' in text: # Find double quotes and converted quotes matched = [m.group() for m in re.finditer(r'[(``)(\'\')(")]+', text)] # Replace converted quotes back to double quotes tokens = [matched.pop(0) if tok in ['"', "``", "''"] else tok for tok in raw_tokens] else: tokens = raw_tokens return align_tokens(tokens, text)
[docs]class TreebankWordDetokenizer(TokenizerI): """ The Treebank detokenizer uses the reverse regex operations corresponding to the Treebank tokenizer's regexes. Note: - There're additional assumption mades when undoing the padding of [;@#$%&] punctuation symbols that isn't presupposed in the TreebankTokenizer. - There're additional regexes added in reversing the parentheses tokenization, - the r'([\]\)\}\>])\s([:;,.])' removes the additional right padding added to the closing parentheses precedding [:;,.]. - It's not possible to return the original whitespaces as they were because there wasn't explicit records of where '\n', '\t' or '\s' were removed at the text.split() operation. >>> from nltk.tokenize.treebank import TreebankWordTokenizer, TreebankWordDetokenizer >>> s = '''Good muffins cost $3.88\\nin New York. Please buy me\\ntwo of them.\\nThanks.''' >>> d = TreebankWordDetokenizer() >>> t = TreebankWordTokenizer() >>> toks = t.tokenize(s) >>> d.detokenize(toks) 'Good muffins cost $3.88 in New York. Please buy me two of them. Thanks.' The MXPOST parentheses substitution can be undone using the `convert_parentheses` parameter: >>> s = '''Good muffins cost $3.88\\nin New (York). Please (buy) me\\ntwo of them.\\n(Thanks).''' >>> expected_tokens = ['Good', 'muffins', 'cost', '$', '3.88', 'in', ... 'New', '-LRB-', 'York', '-RRB-', '.', 'Please', '-LRB-', 'buy', ... '-RRB-', 'me', 'two', 'of', 'them.', '-LRB-', 'Thanks', '-RRB-', '.'] >>> expected_tokens == t.tokenize(s, convert_parentheses=True) True >>> expected_detoken = 'Good muffins cost $3.88 in New (York). Please (buy) me two of them. (Thanks).' >>> expected_detoken == d.detokenize(t.tokenize(s, convert_parentheses=True), convert_parentheses=True) True During tokenization it's safe to add more spaces but during detokenization, simply undoing the padding doesn't really help. - During tokenization, left and right pad is added to [!?], when detokenizing, only left shift the [!?] is needed. Thus (re.compile(r'\s([?!])'), r'\g<1>') - During tokenization [:,] are left and right padded but when detokenizing, only left shift is necessary and we keep right pad after comma/colon if the string after is a non-digit. Thus (re.compile(r'\s([:,])\s([^\d])'), r'\1 \2') >>> from nltk.tokenize.treebank import TreebankWordDetokenizer >>> toks = ['hello', ',', 'i', 'ca', "n't", 'feel', 'my', 'feet', '!', 'Help', '!', '!'] >>> twd = TreebankWordDetokenizer() >>> twd.detokenize(toks) "hello, i can't feel my feet! Help!!" >>> toks = ['hello', ',', 'i', "can't", 'feel', ';', 'my', 'feet', '!', ... 'Help', '!', '!', 'He', 'said', ':', 'Help', ',', 'help', '?', '!'] >>> twd.detokenize(toks) "hello, i can't feel; my feet! Help!! He said: Help, help?!" """ _contractions = MacIntyreContractions() CONTRACTIONS2 = [re.compile(pattern.replace('(?#X)', '\s')) for pattern in _contractions.CONTRACTIONS2] CONTRACTIONS3 = [re.compile(pattern.replace('(?#X)', '\s')) for pattern in _contractions.CONTRACTIONS3] #ending quotes ENDING_QUOTES = [ (re.compile(r"([^' ])\s('ll|'LL|'re|'RE|'ve|'VE|n't|N'T) "), r"\1\2 "), (re.compile(r"([^' ])\s('[sS]|'[mM]|'[dD]|') "), r"\1\2 "), (re.compile(r'(\S)(\'\')'), r'\1\2 '), (re.compile(r" '' "), '"') ] # Handles double dashes DOUBLE_DASHES = (re.compile(r' -- '), r'--') # Optionally: Convert parentheses, brackets and converts them from PTB symbols. CONVERT_PARENTHESES = [ (re.compile('-LRB-'), '('), (re.compile('-RRB-'), ')'), (re.compile('-LSB-'), '['), (re.compile('-RSB-'), ']'), (re.compile('-LCB-'), '{'), (re.compile('-RCB-'), '}') ] # Undo padding on parentheses. PARENS_BRACKETS = [(re.compile(r'\s([\[\(\{\<])\s'), r' \g<1>'), (re.compile(r'\s([\]\)\}\>])\s'), r'\g<1> '), (re.compile(r'([\]\)\}\>])\s([:;,.])'), r'\1\2')] #punctuation PUNCTUATION = [ (re.compile(r"([^'])\s'\s"), r"\1' "), (re.compile(r'\s([?!])'), r'\g<1>'), # Strip left pad for [?!] #(re.compile(r'\s([?!])\s'), r'\g<1>'), (re.compile(r'([^\.])\s(\.)([\]\)}>"\']*)\s*$'), r'\1\2\3'), # When tokenizing, [;@#$%&] are padded with whitespace regardless of # whether there are spaces before or after them. # But during detokenization, we need to distinguish between left/right # pad, so we split this up. (re.compile(r'\s([#$])\s'), r' \g<1>'), # Left pad. (re.compile(r'\s([;%])\s'), r'\g<1> '), # Right pad. (re.compile(r'\s([&])\s'), r' \g<1> '), # Unknown pad. (re.compile(r'\s\.\.\.\s'), r'...'), (re.compile(r'\s([:,])\s$'), r'\1'), (re.compile(r'\s([:,])\s([^\d])'), r'\1 \2') # Keep right pad after comma/colon before non-digits. #(re.compile(r'\s([:,])\s([^\d])'), r'\1\2') ] #starting quotes STARTING_QUOTES = [ (re.compile(r'([ (\[{<])\s``'), r'\1"'), (re.compile(r'\s(``)\s'), r'\1'), (re.compile(r'^``'), r'\"'), ]
[docs] def tokenize(self, tokens, convert_parentheses=False): """ Python port of the Moses detokenizer. :param tokens: A list of strings, i.e. tokenized text. :type tokens: list(str) :return: str """ text = ' '.join(tokens) # Reverse the contractions regexes. # Note: CONTRACTIONS4 are not used in tokenization. for regexp in self.CONTRACTIONS3: text = regexp.sub(r'\1\2', text) for regexp in self.CONTRACTIONS2: text = regexp.sub(r'\1\2', text) # Reverse the regexes applied for ending quotes. for regexp, substitution in self.ENDING_QUOTES: text = regexp.sub(substitution, text) # Undo the space padding. text = text.strip() # Reverse the padding on double dashes. regexp, substitution = self.DOUBLE_DASHES text = regexp.sub(substitution, text) if convert_parentheses: for regexp, substitution in self.CONVERT_PARENTHESES: text = regexp.sub(substitution, text) # Reverse the padding regexes applied for parenthesis/brackets. for regexp, substitution in self.PARENS_BRACKETS: text = regexp.sub(substitution, text) # Reverse the regexes applied for punctuations. for regexp, substitution in self.PUNCTUATION: text = regexp.sub(substitution, text) # Reverse the regexes applied for starting quotes. for regexp, substitution in self.STARTING_QUOTES: text = regexp.sub(substitution, text) return text.strip()
[docs] def detokenize(self, tokens, convert_parentheses=False): """ Duck-typing the abstract *tokenize()*.""" return self.tokenize(tokens, convert_parentheses)