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A proxy object which is used to stand in for a corpus object
before the corpus is loaded.  This allows NLTK to create an object
for each corpus, but defer the costs associated with loading those
corpora until the first time that they're actually accessed.

The first time this object is accessed in any way, it will load
the corresponding corpus, and transform itself into that corpus
(by modifying its own ``__class__`` and ``__dict__`` attributes).

If the corpus can not be found, then accessing this object will(more...)

src/c/l/clojure-nltk-HEAD/resources/nltk/collocations.py   clojure-nltk(Download)
        compare_scorer = BigramAssocMeasures.raw_freq
 
    from nltk.corpus import stopwords, webtext
 
    ignored_words = stopwords.words('english')

src/n/l/nltk-2.0.4/nltk/collocations.py   nltk(Download)
        compare_scorer = BigramAssocMeasures.raw_freq
 
    from nltk.corpus import stopwords, webtext
 
    ignored_words = stopwords.words('english')

src/c/l/clojure-nltk-HEAD/resources/nltk/tokenize/texttiling.py   clojure-nltk(Download)
 
        if stopwords is None:
            from nltk.corpus import stopwords
            stopwords = stopwords.words('english')
        self.__dict__.update(locals())

src/c/l/clojure-nltk-HEAD/resources/nltk/stem/snowball.py   clojure-nltk(Download)
"""
 
from nltk.corpus import stopwords
from nltk.stem import porter
 

src/c/l/clojure-nltk-HEAD/resources/nltk/text.py   clojure-nltk(Download)
 
            print "Building collocations list"
            from nltk.corpus import stopwords
            ignored_words = stopwords.words('english')
            finder = BigramCollocationFinder.from_words(self.tokens, window_size)

src/n/l/nltk-2.0.4/nltk/tokenize/texttiling.py   nltk(Download)
 
        if stopwords is None:
            from nltk.corpus import stopwords
            stopwords = stopwords.words('english')
        self.__dict__.update(locals())

src/n/l/nltk-2.0.4/nltk/stem/snowball.py   nltk(Download)
"""
 
from nltk.corpus import stopwords
from nltk.stem import porter
 

src/n/l/nltk-2.0.4/nltk/text.py   nltk(Download)
 
            print "Building collocations list"
            from nltk.corpus import stopwords
            ignored_words = stopwords.words('english')
            finder = BigramCollocationFinder.from_words(self.tokens, window_size)

src/t/w/tweeql-0.2.4/tweeql/extras/sentiment/analysis.py   tweeql(Download)
from nltk.classify import NaiveBayesClassifier
from nltk.corpus import movie_reviews, stopwords
from nltk.collocations import BigramCollocationFinder
from nltk.metrics import BigramAssocMeasures
from nltk.probability import FreqDist, ConditionalFreqDist

src/t/e/TextComparison-HEAD/exercises/Exercise02_Stems.py   TextComparison(Download)
import Tkinter as tk
from nltk.corpus import reuters
from nltk.corpus import stopwords
from nltk.probability import FreqDist
 

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