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>>> import nltk.data
>>> text = '''
... Punkt knows that the periods in Mr. Smith and Johann S. Bach
... do not mark sentence boundaries. And sometimes sentences
... can start with non-capitalized words. i is a good variable
... name.
... '''
>>> sent_detector = nltk . data . load ( 'tokenizers/punkt/english.pickle' )
>>> print ( ' \n ----- \n ' . join ( sent_detector . tokenize ( text . strip ())))
Punkt knows that the periods in Mr. Smith and Johann S. Bach
do not mark sentence boundaries.
-----
And sometimes sentences
can start with non-capitalized words.
-----
i is a good variable
name.

>>> text = '''
... (How does it deal with this parenthesis?) "It should be part of the
... previous sentence." "(And the same with this one.)" ('And this one!')
... "('(And (this)) '?)" [(and this. )]
... '''
>>> print ( ' \n ----- \n ' . join (
... sent_detector . tokenize ( text . strip ())))
(How does it deal with this parenthesis?)
-----
"It should be part of the
previous sentence."
-----
"(And the same with this one.)"
-----
('And this one!')
-----
"('(And (this)) '?)"
-----
[(and this. )]
>>> print ( ' \n ----- \n ' . join (
... sent_detector . tokenize ( text . strip (), realign_boundaries = False )))
(How does it deal with this parenthesis?
-----
) "It should be part of the
previous sentence.
-----
" "(And the same with this one.
-----
)" ('And this one!
-----
')
"('(And (this)) '?
-----
)" [(and this.
-----
)]

Kiss , Tibor and Strunk , Jan ( 2006 ): Unsupervised Multilingual Sentence
Boundary Detection . Computational Linguistics 32 : 485 - 525.

This tokenizer divides a text into a list of sentences
by using an unsupervised algorithm to build a model for abbreviation
words, collocations, and words that start sentences. It must be
trained on a large collection of plaintext in the target language
before it can be used.
The NLTK data package includes a pre-trained Punkt tokenizer for
English.
(Note that whitespace from the original text, including newlines, is
retained in the output.)
Punctuation following sentences is also included by default
(from NLTK 3.0 onwards). It can be excluded with the realign_boundaries
flag.
However, Punkt is designed to learn parameters (a list of abbreviations, etc.)
unsupervised from a corpus similar to the target domain. The pre-packaged models
may therefore be unsuitable: use PunktSentenceTokenizer(text) to learn
parameters from the given text.
PunktTrainer learns parameters such as a list of abbreviations
(without supervision) from portions of text. Using a PunktTrainer directly
allows for incremental training and modification of the hyper-parameters used
to decide what is considered an abbreviation, etc.
The algorithm for this tokenizer is described in:
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.
Characters which are candidates for sentence boundaries
sentence internal punctuation, which indicates an abbreviation if
preceded by a period-final token.
Used to realign punctuation that should be included in a sentence
although it follows the period (or ?, !).
Tokenize a string to split off punctuation other than periods
Compiles and returns a regular expression to find contexts
including possible sentence boundaries.
Stores data used to perform sentence boundary detection with Punkt.
A set of word types for known abbreviations.
A set of word type tuples for known common collocations
where the first word ends in a period. E.g., (‘S.’, ‘Bach’)
is a common collocation in a text that discusses ‘Johann
S. Bach’. These count as negative evidence for sentence
boundaries.
A set of word types for words that often appear at the
beginning of sentences.
A dictionary mapping word types to the set of orthographic
contexts that word type appears in. Contexts are represented
by adding orthographic context flags: …
Stores a token of text with annotations produced during
sentence boundary detection.
The type with its final period removed if it has one.
The type with its final period removed if it is marked as a
sentence break.
True if the token’s first character is uppercase.
True if the token’s first character is lowercase.
True if the token text is that of an ellipsis.
True if the token text is that of a number.
True if the token text is that of an initial.
True if the token text is all alphabetic.
True if the token is either a number or is alphabetic.
Includes common components of PunktTrainer and PunktSentenceTokenizer.
Learns parameters used in Punkt sentence boundary detection.
Calculates and returns parameters for sentence boundary detection as
derived from training.
cut-off value whether a ‘token’ is an abbreviation
allows the disabling of the abbreviation penalty heuristic, which
exponentially disadvantages words that are found at times without a
final period.
upper cut-off for Mikheev’s(2002) abbreviation detection algorithm
minimal log-likelihood value that two tokens need to be considered
as a collocation
minimal log-likelihood value that a token requires to be considered
as a frequent sentence starter
this includes as potential collocations all word pairs where the first
word ends in a period. It may be useful in corpora where there is a lot
of variation that makes abbreviations like Mr difficult to identify.
this includes as potential collocations all word pairs where the first
word is an abbreviation. Such collocations override the orthographic
heuristic, but not the sentence starter heuristic. This is overridden by
INCLUDE_ALL_COLLOCS, and if both are false, only collocations with initials
and ordinals are considered.
this sets a minimum bound on the number of times a bigram needs to
appear before it can be considered a collocation, in addition to log
likelihood statistics. This is useful when INCLUDE_ALL_COLLOCS is True.
Collects training data from a given text. If finalize is True, it
will determine all the parameters for sentence boundary detection. If
not, this will be delayed until get_params() or finalize_training() is
called. If verbose is True, abbreviations found will be listed.
Collects training data from a given list of tokens.
Uses data that has been gathered in training to determine likely
collocations and sentence starters.
Allows memory use to be reduced after much training by removing data
about rare tokens that are unlikely to have a statistical effect with
further training. Entries occurring above the given thresholds will be
retained.
Recalculates abbreviations given type frequencies, despite no prior
determination of abbreviations.
This fails to include abbreviations otherwise found as “rare”.
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.
train_text can either be the sole training text for this sentence
boundary detector, or can be a PunktParameters object.
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.
Given a text, returns a list of the sentences in that 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.
Given a text, generates (start, end) spans of sentences
in the text.
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.
Returns True if the given text includes a sentence break.
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 .
Given a sequence of tokens, generates lists of tokens, each list
corresponding to a sentence.
Builds a punkt model and applies it to the same text

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From Wikipedia, the free encyclopedia
Anatomical detail of human female sexual organ
For other uses, see G-spot (disambiguation) .
Drawing of the female internal sexual anatomy The G-spot (6) is reportedly located 5–8 cm (2–3 in) into the vagina, at the side of the urethra (9) and the urinary bladder (3)


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^ Jump up to: a b c d e f Hines T (August 2001). "The G-Spot: A modern gynecologic myth". Am J Obstet Gynecol . 185 (2): 359–62. doi : 10.1067/mob.2001.115995 . PMID 11518892 . S2CID 32381437 .

^ Jump up to: a b c d e f g Kilchevsky, A; Vardi, Y; Lowenstein, L; Gruenwald, I (January 2012). "Is the Female G-Spot Truly a Distinct Anatomic Entity?". The Journal of Sexual Medicine . 9 (3): 719–26. doi : 10.1111/j.1743-6109.2011.02623.x . PMID 22240236 .
"G-Spot Does Not Exist, 'Without A Doubt,' Say Researchers" . The Huffington Post . January 19, 2012.

^ Jump up to: a b c O'Connell, H. E.; Sanjeevan, K. V.; Hutson, J. M. (October 2005). "Anatomy of the clitoris". The Journal of Urology . 174 (4 Pt 1): 1189–95. doi : 10.1097/01.ju.0000173639.38898.cd . PMID 16145367 . S2CID 26109805 .
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