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## `nltk/tokenize/punkt.py` — `class:PunktLanguageVars` (lines 182-293)
```python
class PunktLanguageVars:
"""
Stores variables, mostly regular expressions, which may be
language-dependent for correct application of the algorithm.
An extension of this class may modify its properties to suit
a language other than English; an instance can then be passed
as an argument to PunktSentenceTokenizer and PunktTrainer
constructors.
"""
__slots__ = ("_re_period_context", "_re_word_tokenizer")
def __getstate__(self):
# All modifications to the class are performed by inheritance.
# Non-default parameters to be pickled must be defined in the inherited
# class.
return 1
def __setstate__(self, state):
return 1
sent_end_chars = (".", "?", "!")
"""Characters which are candidates for sentence boundaries"""
@property
def _re_sent_end_chars(self):
return "[%s]" % re.escape("".join(self.sent_end_chars))
internal_punctuation = ",:;" # might want to extend this..
"""sentence internal punctuation, which indicates an abbreviation if
preceded by a period-final token."""
re_boundary_realignment = re.compile(r'["\')\]}]+?(?:\s+|(?=--)|$)', re.MULTILINE)
"""Used to realign punctuation that should be included in a sentence
although it follows the period (or ?, !)."""
_re_word_start = r"[^\(\"\`{\[:;&\#\*@\)}\]\-,]"
"""Excludes some characters from starting word tokens"""
@property
def _re_non_word_chars(self):
return r"(?:[)\";}\]\*:@\'\({\[%s])" % re.escape(
"".join(set(self.sent_end_chars) - {"."})
)
"""Characters that cannot appear within words"""
_re_multi_char_punct = r"(?:\-{2,}|\.{2,}|(?:\.\s){2,}\.)"
"""Hyphen and ellipsis are multi-character punctuation"""
_word_tokenize_fmt = r"""(
%(MultiChar)s
|
(?=%(WordStart)s)\S+? # Accept word characters until end is found
(?= # Sequences marking a word's end
\s| # White-space
$| # End-of-string
%(NonWord)s|%(MultiChar)s| # Punctuation
,(?=$|\s|%(NonWord)s|%(MultiChar)s) # Comma if at end of word
)
|
\S
)"""
"""Format of a regular expression to split punctuation from words,
excluding period."""
def _word_tokenizer_re(self):
"""Compiles and returns a regular expression for word tokenization"""
try:
return self._re_word_tokenizer
except AttributeError:
self._re_word_tokenizer = re.compile(
self._word_tokenize_fmt
% {
"NonWord": self._re_non_word_chars,
"MultiChar": self._re_multi_char_punct,
"WordStart": self._re_word_start,
},
re.UNICODE | re.VERBOSE,
)
return self._re_word_tokenizer
def word_tokenize(self, s):
"""Tokenize a string to split off punctuation other than periods"""
return self._word_tokenizer_re().findall(s)
_period_context_fmt = r"""
%(SentEndChars)s # a potential sentence ending
(?=(?P<after_tok>
%(NonWord)s # either other punctuation
|
\s+(?P<next_tok>\S+) # or whitespace and some other token
))"""
"""Format of a regular expression to find contexts including possible
sentence boundaries. Matches token which the possible sentence boundary
ends, and matches the following token within a lookahead expression."""
def period_context_re(self):
"""Compiles and returns a regular expression to find contexts
including possible sentence boundaries."""
try:
return self._re_period_context
except:
self._re_period_context = re.compile(
self._period_context_fmt
% {
"NonWord": self._re_non_word_chars,
"SentEndChars": self._re_sent_end_chars,
},
re.UNICODE | re.VERBOSE,
)
return self._re_period_context
```
## `nltk/tokenize/punkt.py` — `class:PunktSentenceTokenizer` (lines 1235-1691)
```python
class PunktSentenceTokenizer(PunktBaseClass, TokenizerI):
"""
A sentence tokenizer which uses an unsupervised algorithm to build
a model for abbreviation words, collocations, and words that start
sentences; and then uses that model to find sentence boundaries.
This approach has been shown to work well for many European
languages.
"""
def __init__(
self, train_text=None, verbose=False, lang_vars=None, token_cls=PunktToken
):
"""
train_text can either be the sole training text for this sentence
boundary detector, or can be a PunktParameters object.
"""
PunktBaseClass.__init__(self, lang_vars=lang_vars, token_cls=token_cls)
if train_text:
self._params = self.train(train_text, verbose)
def train(self, train_text, verbose=False):
"""
Derives parameters from a given training text, or uses the parameters
given. Repeated calls to this method destroy previous parameters. For
incremental training, instantiate a separate PunktTrainer instance.
"""
if not isinstance(train_text, str):
return train_text
return PunktTrainer(
train_text, lang_vars=self._lang_vars, token_cls=self._Token
).get_params()
# ////////////////////////////////////////////////////////////
# { Tokenization
# ////////////////////////////////////////////////////////////
def tokenize(self, text, realign_boundaries=True):
"""
Given a text, returns a list of the sentences in that text.
"""
return list(self.sentences_from_text(text, realign_boundaries))
def debug_decisions(self, text):
"""
Classifies candidate periods as sentence breaks, yielding a dict for
each that may be used to understand why the decision was made.
See format_debug_decision() to help make this output readable.
"""
for match, decision_text in self._match_potential_end_contexts(text):
tokens = self._tokenize_words(decision_text)
tokens = list(self._annotate_first_pass(tokens))
while tokens and not tokens[0].tok.endswith(self._lang_vars.sent_end_chars):
tokens.pop(0)
yield {
"period_index": match.end() - 1,
"text": decision_text,
"type1": tokens[0].type,
"type2": tokens[1].type,
"type1_in_abbrs": bool(tokens[0].abbr),
"type1_is_initial": bool(tokens[0].is_initial),
"type2_is_sent_starter": tokens[1].type_no_sentperiod
in self._params.sent_starters,
"type2_ortho_heuristic": self._ortho_heuristic(tokens[1]),
"type2_ortho_contexts": set(
self._params._debug_ortho_context(tokens[1].type_no_sentperiod)
),
"collocation": (
tokens[0].type_no_sentperiod,
tokens[1].type_no_sentperiod,
)
in self._params.collocations,
"reason": self._second_pass_annotation(tokens[0], tokens[1])
or REASON_DEFAULT_DECISION,
"break_decision": tokens[0].sentbreak,
}
def span_tokenize(self, text, realign_boundaries=True):
"""
Given a text, generates (start, end) spans of sentences
in the text.
"""
slices = self._slices_from_text(text)
if realign_boundaries:
slices = self._realign_boundaries(text, slices)
for sentence in slices:
yield (sentence.start, sentence.stop)
def sentences_from_text(self, text, realign_boundaries=True):
"""
Given a text, generates the sentences in that text by only
testing candidate sentence breaks. If realign_boundaries is
True, includes in the sentence closing punctuation that
follows the period.
"""
return [text[s:e] for s, e in self.span_tokenize(text, realign_boundaries)]
def _match_potential_end_contexts(self, text):
"""
Given a text, find the matches of potential sentence breaks,
alongside the contexts surrounding these sentence breaks.
Since the fix for the ReDOS discovered in issue #2866, we no longer match
the word before a potential end of sentence token. Instead, we use a separate
regex for this. As a consequence, `finditer`'s desire to find non-overlapping
matches no longer aids us in finding the single longest match.
Where previously, we could use::
>>> pst = PunktSentenceTokenizer()
>>> text = "Very bad acting!!! I promise."
>>> list(pst._lang_vars.period_context_re().finditer(text)) # doctest: +SKIP
[<re.Match object; span=(9, 18), match='acting!!!'>]
Now we have to find the word before (i.e. 'acting') separately, and `finditer`
returns::
>>> pst = PunktSentenceTokenizer()
>>> text = "Very bad acting!!! I promise."
>>> list(pst._lang_vars.period_context_re().finditer(text)) # doctest: +NORMALIZE_WHITESPACE
[<re.Match object; span=(15, 16), match='!'>,
<re.Match object; span=(16, 17), match='!'>,
<re.Match object; span=(17, 18), match='!'>]
So, we need to find the word before the match from right to left, and then manually remove
the overlaps. That is what this method does::
>>> pst = PunktSentenceTokenizer()
>>> text = "Very bad acting!!! I promise."
>>> pst._match_potential_end_contexts(text)
[(<re.Match object; span=(17, 18), match='!'>, 'acting!!! I')]
:param text: String of one or more sentences
:type text: str
:return: List of match-context tuples.
:rtype: List[Tuple[re.Match, str]]
"""
before_words = {}
matches = []
for match in reversed(list(self._lang_vars.period_context_re().finditer(text))):
# Ignore matches that have already been captured by matches to the right of this match
if matches and match.end() > before_start:
continue
# Find the word before the current match
split = text[: match.start()].rsplit(maxsplit=1)
before_start = len(split[0]) if len(split) == 2 else 0
before_words[match] = split[-1]
matches.append(match)
return [
(
match,
before_words[match] + match.group() + match.group("after_tok"),
)
for match in matches[::-1]
]
def _slices_from_text(self, text):
last_break = 0
for match, context in self._match_potential_end_contexts(text):
if self.text_contains_sentbreak(context):
yield slice(last_break, match.end())
if match.group("next_tok"):
# next sentence starts after whitespace
last_break = match.start("next_tok")
else:
# next sentence starts at following punctuation
last_break = match.end()
# The last sentence should not contain trailing whitespace.
yield slice(last_break, len(text.rstrip()))
def _realign_boundaries(self, text, slices):
"""
Attempts to realign punctuation that falls after the period but
should otherwise be included in the same sentence.
For example: "(Sent1.) Sent2." will otherwise be split as::
["(Sent1.", ") Sent1."].
This method will produce::
["(Sent1.)", "Sent2."].
"""
realign = 0
for sentence1, sentence2 in _pair_iter(slices):
sentence1 = slice(sentence1.start + realign, sentence1.stop)
if not sentence2:
if text[sentence1]:
yield sentence1
continue
m = self._lang_vars.re_boundary_realignment.match(text[sentence2])
if m:
yield slice(sentence1.start, sentence2.start + len(m.group(0).rstrip()))
realign = m.end()
else:
realign = 0
if text[sentence1]:
yield sentence1
def text_contains_sentbreak(self, text):
"""
Returns True if the given text includes a sentence break.
"""
found = False # used to ignore last token
for tok in self._annotate_tokens(self._tokenize_words(text)):
if found:
return True
if tok.sentbreak:
found = True
return False
def sentences_from_text_legacy(self, text):
"""
Given a text, generates the sentences in that text. Annotates all
tokens, rather than just those with possible sentence breaks. Should
produce the same results as ``sentences_from_text``.
"""
tokens = self._annotate_tokens(self._tokenize_words(text))
return self._build_sentence_list(text, tokens)
def sentences_from_tokens(self, tokens):
"""
Given a sequence of tokens, generates lists of tokens, each list
corresponding to a sentence.
"""
tokens = iter(self._annotate_tokens(self._Token(t) for t in tokens))
sentence = []
for aug_tok in tokens:
sentence.append(aug_tok.tok)
if aug_tok.sentbreak:
yield sentence
sentence = []
if sentence:
yield sentence
def _annotate_tokens(self, tokens):
"""
Given a set of tokens augmented with markers for line-start and
paragraph-start, returns an iterator through those tokens with full
annotation including predicted sentence breaks.
"""
# Make a preliminary pass through the document, marking likely
# sentence breaks, abbreviations, and ellipsis tokens.
tokens = self._annotate_first_pass(tokens)
# Make a second pass through the document, using token context
# information to change our preliminary decisions about where
# sentence breaks, abbreviations, and ellipsis occurs.
tokens = self._annotate_second_pass(tokens)
## [XX] TESTING
# tokens = list(tokens)
# self.dump(tokens)
return tokens
def _build_sentence_list(self, text, tokens):
"""
Given the original text and the list of augmented word tokens,
construct and return a tokenized list of sentence strings.
"""
# Most of the work here is making sure that we put the right
# pieces of whitespace back in all the right places.
# Our position in the source text, used to keep track of which
# whitespace to add:
pos = 0
# A regular expression that finds pieces of whitespace:
white_space_regexp = re.compile(r"\s*")
sentence = ""
for aug_tok in tokens:
tok = aug_tok.tok
# Find the whitespace before this token, and update pos.
white_space = white_space_regexp.match(text, pos).group()
pos += len(white_space)
# Some of the rules used by the punkt word tokenizer
# strip whitespace out of the text, resulting in tokens
# that contain whitespace in the source text. If our
# token doesn't match, see if adding whitespace helps.
# If so, then use the version with whitespace.
if text[pos : pos + len(tok)] != tok:
pat = r"\s*".join(re.escape(c) for c in tok)
m = re.compile(pat).match(text, pos)
if m:
tok = m.group()
# Move our position pointer to the end of the token.
assert text[pos : pos + len(tok)] == tok
pos += len(tok)
# Add this token. If it's not at the beginning of the
# sentence, then include any whitespace that separated it
# from the previous token.
if sentence:
sentence += white_space
sentence += tok
# If we're at a sentence break, then start a new sentence.
if aug_tok.sentbreak:
yield sentence
sentence = ""
# If the last sentence is empty, discard it.
if sentence:
yield sentence
# [XX] TESTING
def dump(self, tokens):
print("writing to /tmp/punkt.new...")
with open("/tmp/punkt.new", "w") as outfile:
for aug_tok in tokens:
if aug_tok.parastart:
outfile.write("\n\n")
elif aug_tok.linestart:
outfile.write("\n")
else:
outfile.write(" ")
outfile.write(str(aug_tok))
# ////////////////////////////////////////////////////////////
# { Customization Variables
# ////////////////////////////////////////////////////////////
PUNCTUATION = tuple(";:,.!?")
# ////////////////////////////////////////////////////////////
# { Annotation Procedures
# ////////////////////////////////////////////////////////////
def _annotate_second_pass(self, tokens):
"""
Performs a token-based classification (section 4) over the given
tokens, making use of the orthographic heuristic (4.1.1), collocation
heuristic (4.1.2) and frequent sentence starter heuristic (4.1.3).
"""
for token1, token2 in _pair_iter(tokens):
self._second_pass_annotation(token1, token2)
yield token1
def _second_pass_annotation(self, aug_tok1, aug_tok2):
"""
Performs token-based classification over a pair of contiguous tokens
updating the first.
"""
# Is it the last token? We can't do anything then.
if not aug_tok2:
return
if not aug_tok1.period_final:
# We only care about words ending in periods.
return
typ = aug_tok1.type_no_period
next_typ = aug_tok2.type_no_sentperiod
tok_is_initial = aug_tok1.is_initial
# [4.1.2. Collocation Heuristic] If there's a
# collocation between the word before and after the
# period, then label tok as an abbreviation and NOT
# a sentence break. Note that collocations with
# frequent sentence starters as their second word are
# excluded in training.
if (typ, next_typ) in self._params.collocations:
aug_tok1.sentbreak = False
aug_tok1.abbr = True
return REASON_KNOWN_COLLOCATION
# [4.2. Token-Based Reclassification of Abbreviations] If
# the token is an abbreviation or an ellipsis, then decide
# whether we should *also* classify it as a sentbreak.
if (aug_tok1.abbr or aug_tok1.ellipsis) and (not tok_is_initial):
# [4.1.1. Orthographic Heuristic] Check if there's
# orthogrpahic evidence about whether the next word
# starts a sentence or not.
is_sent_starter = self._ortho_heuristic(aug_tok2)
if is_sent_starter == True:
aug_tok1.sentbreak = True
return REASON_ABBR_WITH_ORTHOGRAPHIC_HEURISTIC
# [4.1.3. Frequent Sentence Starter Heruistic] If the
# next word is capitalized, and is a member of the
# frequent-sentence-starters list, then label tok as a
# sentence break.
if aug_tok2.first_upper and next_typ in self._params.sent_starters:
aug_tok1.sentbreak = True
return REASON_ABBR_WITH_SENTENCE_STARTER
# [4.3. Token-Based Detection of Initials and Ordinals]
# Check if any initials or ordinals tokens that are marked
# as sentbreaks should be reclassified as abbreviations.
if tok_is_initial or typ == "##number##":
# [4.1.1. Orthographic Heuristic] Check if there's
# orthogrpahic evidence about whether the next word
# starts a sentence or not.
is_sent_starter = self._ortho_heuristic(aug_tok2)
if is_sent_starter == False:
aug_tok1.sentbreak = False
aug_tok1.abbr = True
if tok_is_initial:
return REASON_INITIAL_WITH_ORTHOGRAPHIC_HEURISTIC
return REASON_NUMBER_WITH_ORTHOGRAPHIC_HEURISTIC
# Special heuristic for initials: if orthogrpahic
# heuristic is unknown, and next word is always
# capitalized, then mark as abbrev (eg: J. Bach).
if (
is_sent_starter == "unknown"
and tok_is_initial
and aug_tok2.first_upper
and not (self._params.ortho_context[next_typ] & _ORTHO_LC)
):
aug_tok1.sentbreak = False
aug_tok1.abbr = True
return REASON_INITIAL_WITH_SPECIAL_ORTHOGRAPHIC_HEURISTIC
return
def _ortho_heuristic(self, aug_tok):
"""
Decide whether the given token is the first token in a sentence.
"""
# Sentences don't start with punctuation marks:
if aug_tok.tok in self.PUNCTUATION:
return False
ortho_context = self._params.ortho_context[aug_tok.type_no_sentperiod]
# If the word is capitalized, occurs at least once with a
# lower case first letter, and never occurs with an upper case
# first letter sentence-internally, then it's a sentence starter.
if (
aug_tok.first_upper
and (ortho_context & _ORTHO_LC)
and not (ortho_context & _ORTHO_MID_UC)
):
return True
# If the word is lower case, and either (a) we've seen it used
# with upper case, or (b) we've never seen it used
# sentence-initially with lower case, then it's not a sentence
# starter.
if aug_tok.first_lower and (
(ortho_context & _ORTHO_UC) or not (ortho_context & _ORTHO_BEG_LC)
):
return False
# Otherwise, we're not sure.
return "unknown"
```
## `nltk/tokenize/punkt.py` — `function:PunktSentenceTokenizer.debug_decisions` (lines 1278-1312)
```python
def debug_decisions(self, text):
"""
Classifies candidate periods as sentence breaks, yielding a dict for
each that may be used to understand why the decision was made.
See format_debug_decision() to help make this output readable.
"""
for match, decision_text in self._match_potential_end_contexts(text):
tokens = self._tokenize_words(decision_text)
tokens = list(self._annotate_first_pass(tokens))
while tokens and not tokens[0].tok.endswith(self._lang_vars.sent_end_chars):
tokens.pop(0)
yield {
"period_index": match.end() - 1,
"text": decision_text,
"type1": tokens[0].type,
"type2": tokens[1].type,
"type1_in_abbrs": bool(tokens[0].abbr),
"type1_is_initial": bool(tokens[0].is_initial),
"type2_is_sent_starter": tokens[1].type_no_sentperiod
in self._params.sent_starters,
"type2_ortho_heuristic": self._ortho_heuristic(tokens[1]),
"type2_ortho_contexts": set(
self._params._debug_ortho_context(tokens[1].type_no_sentperiod)
),
"collocation": (
tokens[0].type_no_sentperiod,
tokens[1].type_no_sentperiod,
)
in self._params.collocations,
"reason": self._second_pass_annotation(tokens[0], tokens[1])
or REASON_DEFAULT_DECISION,
"break_decision": tokens[0].sentbreak,
}
```
## `nltk/tokenize/punkt.py` — `function:PunktSentenceTokenizer._match_potential_end_contexts` (lines 1334-1391)
```python
def _match_potential_end_contexts(self, text):
"""
Given a text, find the matches of potential sentence breaks,
alongside the contexts surrounding these sentence breaks.
Since the fix for the ReDOS discovered in issue #2866, we no longer match
the word before a potential end of sentence token. Instead, we use a separate
regex for this. As a consequence, `finditer`'s desire to find non-overlapping
matches no longer aids us in finding the single longest match.
Where previously, we could use::
>>> pst = PunktSentenceTokenizer()
>>> text = "Very bad acting!!! I promise."
>>> list(pst._lang_vars.period_context_re().finditer(text)) # doctest: +SKIP
[<re.Match object; span=(9, 18), match='acting!!!'>]
Now we have to find the word before (i.e. 'acting') separately, and `finditer`
returns::
>>> pst = PunktSentenceTokenizer()
>>> text = "Very bad acting!!! I promise."
>>> list(pst._lang_vars.period_context_re().finditer(text)) # doctest: +NORMALIZE_WHITESPACE
[<re.Match object; span=(15, 16), match='!'>,
<re.Match object; span=(16, 17), match='!'>,
<re.Match object; span=(17, 18), match='!'>]
So, we need to find the word before the match from right to left, and then manually remove
the overlaps. That is what this method does::
>>> pst = PunktSentenceTokenizer()
>>> text = "Very bad acting!!! I promise."
>>> pst._match_potential_end_contexts(text)
[(<re.Match object; span=(17, 18), match='!'>, 'acting!!! I')]
:param text: String of one or more sentences
:type text: str
:return: List of match-context tuples.
:rtype: List[Tuple[re.Match, str]]
"""
before_words = {}
matches = []
for match in reversed(list(self._lang_vars.period_context_re().finditer(text))):
# Ignore matches that have already been captured by matches to the right of this match
if matches and match.end() > before_start:
continue
# Find the word before the current match
split = text[: match.start()].rsplit(maxsplit=1)
before_start = len(split[0]) if len(split) == 2 else 0
before_words[match] = split[-1]
matches.append(match)
return [
(
match,
before_words[match] + match.group() + match.group("after_tok"),
)
for match in matches[::-1]
]
```
## `nltk/tokenize/punkt.py` — `function:PunktSentenceTokenizer._slices_from_text` (lines 1393-1405)
```python
def _slices_from_text(self, text):
last_break = 0
for match, context in self._match_potential_end_contexts(text):
if self.text_contains_sentbreak(context):
yield slice(last_break, match.end())
if match.group("next_tok"):
# next sentence starts after whitespace
last_break = match.start("next_tok")
else:
# next sentence starts at following punctuation
last_break = match.end()
# The last sentence should not contain trailing whitespace.
yield slice(last_break, len(text.rstrip()))
```
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