g-TdZddlZddlZddlmZddlmZmZddlm Z ddl m Z ddl m Z ddlmZmZdd lmZmZmZmZmZmZmZdd lmZdd lmZmZdd lmZm Z m!Z!m"Z"dd l#m$Z$ddl%m&Z&ddl'm(Z(ddl)m*Z*ddl+m,Z,m-Z-ddl.m/Z/m0Z0ejbddZ2ddZ3e Z4ddZ5y)a@ NLTK Tokenizer Package Tokenizers divide strings into lists of substrings. For example, tokenizers can be used to find the words and punctuation in a string: >>> from nltk.tokenize import word_tokenize >>> s = '''Good muffins cost $3.88\nin New York. Please buy me ... two of them.\n\nThanks.''' >>> word_tokenize(s) # doctest: +NORMALIZE_WHITESPACE ['Good', 'muffins', 'cost', '$', '3.88', 'in', 'New', 'York', '.', 'Please', 'buy', 'me', 'two', 'of', 'them', '.', 'Thanks', '.'] This particular tokenizer requires the Punkt sentence tokenization models to be installed. NLTK also provides a simpler, regular-expression based tokenizer, which splits text on whitespace and punctuation: >>> from nltk.tokenize import wordpunct_tokenize >>> wordpunct_tokenize(s) # doctest: +NORMALIZE_WHITESPACE ['Good', 'muffins', 'cost', '$', '3', '.', '88', 'in', 'New', 'York', '.', 'Please', 'buy', 'me', 'two', 'of', 'them', '.', 'Thanks', '.'] We can also operate at the level of sentences, using the sentence tokenizer directly as follows: >>> from nltk.tokenize import sent_tokenize, word_tokenize >>> sent_tokenize(s) ['Good muffins cost $3.88\nin New York.', 'Please buy me\ntwo of them.', 'Thanks.'] >>> [word_tokenize(t) for t in sent_tokenize(s)] # doctest: +NORMALIZE_WHITESPACE [['Good', 'muffins', 'cost', '$', '3.88', 'in', 'New', 'York', '.'], ['Please', 'buy', 'me', 'two', 'of', 'them', '.'], ['Thanks', '.']] Caution: when tokenizing a Unicode string, make sure you are not using an encoded version of the string (it may be necessary to decode it first, e.g. with ``s.decode("utf8")``. NLTK tokenizers can produce token-spans, represented as tuples of integers having the same semantics as string slices, to support efficient comparison of tokenizers. (These methods are implemented as generators.) >>> from nltk.tokenize import WhitespaceTokenizer >>> list(WhitespaceTokenizer().span_tokenize(s)) # doctest: +NORMALIZE_WHITESPACE [(0, 4), (5, 12), (13, 17), (18, 23), (24, 26), (27, 30), (31, 36), (38, 44), (45, 48), (49, 51), (52, 55), (56, 58), (59, 64), (66, 73)] There are numerous ways to tokenize text. If you need more control over tokenization, see the other methods provided in this package. For further information, please see Chapter 3 of the NLTK book. N)load)TweetTokenizercasual_tokenize)NLTKWordTokenizer)LegalitySyllableTokenizer) MWETokenizer)PunktSentenceTokenizerPunktTokenizer)BlanklineTokenizerRegexpTokenizerWhitespaceTokenizerWordPunctTokenizerblankline_tokenizeregexp_tokenizewordpunct_tokenize) ReppTokenizer)SExprTokenizersexpr_tokenize) LineTokenizerSpaceTokenizer TabTokenizer line_tokenize)SyllableTokenizer)StanfordSegmenter)TextTilingTokenizer)ToktokTokenizer)TreebankWordDetokenizerTreebankWordTokenizer)regexp_span_tokenizestring_span_tokenizect|S)z A constructor for the PunktTokenizer that utilizes a lru cache for performance. :param language: the model name in the Punkt corpus :type language: str )r )languages K/var/www/openai/venv/lib/python3.12/site-packages/nltk/tokenize/__init__.py_get_punkt_tokenizerr$`s ( ##c:t|}|j|S)a Return a sentence-tokenized copy of *text*, using NLTK's recommended sentence tokenizer (currently :class:`.PunktSentenceTokenizer` for the specified language). :param text: text to split into sentences :param language: the model name in the Punkt corpus )r$tokenize)textr" tokenizers r# sent_tokenizer*ms%X.I   d ##r%c|r|gn t||}|Dcgc]}tj|D]}| c}}Scc}}w)a Return a tokenized copy of *text*, using NLTK's recommended word tokenizer (currently an improved :class:`.TreebankWordTokenizer` along with :class:`.PunktSentenceTokenizer` for the specified language). :param text: text to split into words :type text: str :param language: the model name in the Punkt corpus :type language: str :param preserve_line: A flag to decide whether to sentence tokenize the text or not. :type preserve_line: bool )r*_treebank_word_tokenizerr')r(r" preserve_line sentencessenttokens r# word_tokenizer1sM(]4-JI# #$1I1R1RSW1X1X)  s#>)english)r2F)6__doc__ functoolsre nltk.datarnltk.tokenize.casualrrnltk.tokenize.destructiver nltk.tokenize.legality_principlernltk.tokenize.mwernltk.tokenize.punktr r nltk.tokenize.regexpr r r rrrrnltk.tokenize.repprnltk.tokenize.sexprrrnltk.tokenize.simplerrrr!nltk.tokenize.sonority_sequencingr nltk.tokenize.stanford_segmenterrnltk.tokenize.texttilingrnltk.tokenize.toktokrnltk.tokenize.treebankrrnltk.tokenize.utilrr lru_cacher$r*r,r1r%r#rHs2h @7F*F-> @>80QI $$ $-.r%