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======== NLTK has developed an extensive library of Python modules and other resources to support the analysis of natural language. This documentation covers Python modules only. The NLTK Website: ------------------------------------------------------------------------- Installing ========== Python is usually the most convenient environment for NLTK and it is possible to use NLTK with any version of Python. For Windows users, the NLTK installer is available from the NLTK website It can be installed using the command line from the Python "install.py" file. For other operating systems, it is recommended to use the distributions' package management tool. For example, the NLTK distribution for Red Hat Linux is available from the project website. The distribution includes all the required components to get NLTK running. For more information on the NLTK installer, see the installation tutorial at: ------------------------------------------------------------------------- Language-Specific Resources ============================ The NLTK Language Documentation provides a broad overview of the NLTK library, its structure, the relationship between the modules, as well as the classes, methods and functions. The NLTK Language Documentation is intended to be a reference guide for the community of NLTK users. It is not a detailed language guide. The NLTK Language Documentation is written in markdown and uses the reStructuredText syntax. See for information on the format. For the English language, the Language Documentation is available at: There are language documentation for other languages at: If you have any questions about the English language documentation, or any other questions about the NLTK Language Documentation, feel free to ask. The documentation for the NLTK Python modules is available in the NLTK Python module documentation pages. If you are looking for comprehensive documentation on the Python module functionality, check the Python documentation: The NLTK installation documentation contains information about installing, configuring and using NLTK. The NLTK documentation includes tutorials for using the NLTK tools and

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- word_tokenize() (plain, standard, unidecode, NLTK Crack Free Download): split text into tokens (words or subword units) - word_tokenize_spanish() (unicode, PLAIN, STD, UNIDE, NLTK): split text into tokens (words or subword units) - nltk.word_tokenize_nlp(text): split text into tokens (words or subword units) - nltk.pos_tag(word_tokenize_nlp(text)): find the token types - nltk.pos_tag(unicode): find the token types - nltk.tag_sents(word_tokenize_nlp(text)): finds the head and tail entities in a chunk - nltk.tag_sents(unicode): finds the head and tail entities in a chunk - nltk.word_tokenize(text): split text into tokens (words or subword units) - nltk.pos_tag(word_tokenize(text)): find the token types - nltk.tag_sents(word_tokenize(text)): finds the head and tail entities in a chunk - nltk.tag_sents(unicode): finds the head and tail entities in a chunk - nltk.noun_chunk(word_tokenize_nlp(text)): find the Noun chunk - nltk.noun_chunk(unicode): find the Noun chunk - nltk.subject_chunk(word_tokenize_nlp(text)): find the subject chunk - nltk.subject_chunk(unicode): find the subject chunk - nltk.verb_chunk(word_tokenize_nlp(text)): find the verb chunk - nltk.verb_chunk(unicode): find the verb chunk - nltk.prep_split_prep(word_tokenize_nlp(text)): process text into tokens (words or subword units) - nltk.lemma_encode(word_tokenize_nlp(text)): pretty-print any kind of text. - nltk.tag_to_labels(word_tokenize_nlp(text)): b78a707d53

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