`nltk.PunktSentenceTokenizer`¶

class nltk.PunktSentenceTokenizer(train_text=None, verbose=False, lang_vars=<nltk.tokenize.punkt.PunktLanguageVars object>, token_cls=<class 'nltk.tokenize.punkt.PunktToken'>)[source]¶: 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.

Methods¶

`__init__`([train_text, verbose, lang_vars, ...])	train_text can either be the sole training text for this sentence
`debug_decisions`(text)	Classifies candidate periods as sentence breaks, yielding a dict for each that may be used to understand why the decision was made.
`dump`(tokens)
`sentences_from_text`(text[, realign_boundaries])	Given a text, generates the sentences in that text by only testing candidate sentence breaks.
`sentences_from_text_legacy`(text)	Given a text, generates the sentences in that text.
`sentences_from_tokens`(tokens)	Given a sequence of tokens, generates lists of tokens, each list corresponding to a sentence.
`span_tokenize`(text[, realign_boundaries])	Given a text, returns a list of the (start, end) spans of sentences in the text.
`span_tokenize_sents`(strings)	Apply `self.span_tokenize()` to each element of `strings`.
`text_contains_sentbreak`(text)	Returns True if the given text includes a sentence break.
`tokenize`(text[, realign_boundaries])	Given a text, returns a list of the sentences in that text.
`tokenize_sents`(strings)	Apply `self.tokenize()` to each element of `strings`.
`train`(train_text[, verbose])	Derives parameters from a given training text, or uses the parameters given.