def demo(scorer=None, compare_scorer=None):
"""Finds trigram collocations in the files of the WebText corpus."""
from nltk.metrics import BigramAssocMeasures, spearman_correlation, ranks_from_scores
if scorer is None:
scorer = BigramAssocMeasures.likelihood_ratio
if compare_scorer is None:
compare_scorer = BigramAssocMeasures.raw_freq
from nltk.corpus import stopwords, webtext
ignored_words = stopwords.words('english')
word_filter = lambda w: len(w) < 3 or w.lower() in ignored_words
for file in webtext.fileids():
words = [word.lower() for word in webtext.words(file)]
cf = BigramCollocationFinder.from_words(words)
cf.apply_freq_filter(3)
cf.apply_word_filter(word_filter)
print(file)
print('\t', [' '.join(tup) for tup in cf.nbest(scorer, 15)])
print('\t Correlation to %s: %0.4f' %
(compare_scorer.__name__,
spearman_correlation(
ranks_from_scores(cf.score_ngrams(scorer)),
ranks_from_scores(cf.score_ngrams(compare_scorer)))))
def demo(scorer=None, compare_scorer=None):
"""Finds bigram collocations in the files of the WebText corpus."""
from nltk.metrics import BigramAssocMeasures, spearman_correlation, ranks_from_scores
if scorer is None:
scorer = BigramAssocMeasures.likelihood_ratio
if compare_scorer is None:
compare_scorer = BigramAssocMeasures.raw_freq
from nltk.corpus import stopwords, webtext
ignored_words = stopwords.words('english')
word_filter = lambda w: len(w) < 3 or w.lower() in ignored_words
for file in webtext.fileids():
words = [word.lower()
for word in webtext.words(file)]
cf = BigramCollocationFinder.from_words(words)
cf.apply_freq_filter(3)
cf.apply_word_filter(word_filter)
print(file)
print('\t', [' '.join(tup) for tup in cf.nbest(scorer, 15)])
print('\t Correlation to %s: %0.4f' % (compare_scorer.__name__,
spearman_correlation(
ranks_from_scores(cf.score_ngrams(scorer)),
ranks_from_scores(cf.score_ngrams(compare_scorer)))))
