nltk.corpus.reader.ProsConsCorpusReader

class documentation

class ProsConsCorpusReader(CategorizedCorpusReader, CorpusReader): (source)

Constructor: ProsConsCorpusReader(root, fileids, word_tokenizer, encoding, **kwargs)

Reader for the Pros and Cons sentence dataset.

>>> from nltk.corpus import pros_cons
>>> pros_cons.sents(categories='Cons')
[['East', 'batteries', '!', 'On', '-', 'off', 'switch', 'too', 'easy',
'to', 'maneuver', '.'], ['Eats', '...', 'no', ',', 'GULPS', 'batteries'],
...]
>>> pros_cons.words('IntegratedPros.txt')
['Easy', 'to', 'use', ',', 'economical', '!', ...]

Method	`__init__`	No summary
Method	`sents`	Return all sentences in the corpus or in the specified files/categories.
Method	`words`	Return all words and punctuation symbols in the corpus or in the specified files/categories.
Method	`_read_sent_block`	Undocumented
Method	`_read_word_block`	Undocumented
Method	`_resolve`	Undocumented
Instance Variable	`_word_tokenizer`	Undocumented

Inherited from CategorizedCorpusReader:

Method	`categories`	Return a list of the categories that are defined for this corpus, or for the file(s) if it is given.
Method	`fileids`	Return a list of file identifiers for the files that make up this corpus, or that make up the given category(s) if specified.
Method	`_add`	Undocumented
Method	`_init`	Undocumented
Instance Variable	`_c2f`	Undocumented
Instance Variable	`_delimiter`	Undocumented
Instance Variable	`_f2c`	Undocumented
Instance Variable	`_file`	Undocumented
Instance Variable	`_map`	Undocumented
Instance Variable	`_pattern`	Undocumented

Inherited from CorpusReader (via CategorizedCorpusReader):

Method	`__repr__`	Undocumented
Method	`abspath`	Return the absolute path for the given file.
Method	`abspaths`	Return a list of the absolute paths for all fileids in this corpus; or for the given list of fileids, if specified.
Method	`citation`	Return the contents of the corpus citation.bib file, if it exists.
Method	`encoding`	Return the unicode encoding for the given corpus file, if known. If the encoding is unknown, or if the given file should be processed using byte strings (str), then return None.
Method	`ensure_loaded`	Load this corpus (if it has not already been loaded). This is used by LazyCorpusLoader as a simple method that can be used to make sure a corpus is loaded -- e.g., in case a user wants to do help(some_corpus).
Method	`license`	Return the contents of the corpus LICENSE file, if it exists.
Method	`open`	Return an open stream that can be used to read the given file. If the file's encoding is not None, then the stream will automatically decode the file's contents into unicode.
Method	`readme`	Return the contents of the corpus README file, if it exists.
Class Variable	`root`	Undocumented
Method	`_get_root`	Undocumented
Instance Variable	`_encoding`	The default unicode encoding for the fileids that make up this corpus. If `encoding` is None, then the file contents are processed using byte strings.
Instance Variable	`_fileids`	A list of the relative paths for the fileids that make up this corpus.
Instance Variable	`_root`	The root directory for this corpus.
Instance Variable	`_tagset`	Undocumented

def __init__(self, root, fileids, word_tokenizer=WordPunctTokenizer(), encoding='utf8', **kwargs): (source) ¶

overrides nltk.corpus.reader.CategorizedCorpusReader.__init__

Parameters
root	The root directory for the corpus.
fileids	a list or regexp specifying the fileids in the corpus.
word_tokenizer	a tokenizer for breaking sentences or paragraphs into words. Default: `WhitespaceTokenizer`
encoding	the encoding that should be used to read the corpus.
**kwargs	additional parameters passed to CategorizedCorpusReader.

def sents(self, fileids=None, categories=None): (source) ¶

Return all sentences in the corpus or in the specified files/categories.

Parameters
fileids	a list or regexp specifying the ids of the files whose sentences have to be returned.
categories	a list specifying the categories whose sentences have to be returned.
Returns
list(list(str))	the given file(s) as a list of sentences. Each sentence is tokenized using the specified word_tokenizer.

def words(self, fileids=None, categories=None): (source) ¶

Return all words and punctuation symbols in the corpus or in the specified files/categories.

Parameters
fileids	a list or regexp specifying the ids of the files whose words have to be returned.
categories	a list specifying the categories whose words have to be returned.
Returns
list(str)	the given file(s) as a list of words and punctuation symbols.

def _read_sent_block(self, stream): (source) ¶

Undocumented

def _read_word_block(self, stream): (source) ¶

Undocumented

def _resolve(self, fileids, categories): (source) ¶

Undocumented

_word_tokenizer = (source) ¶

Undocumented