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    copied!<p>The NLTK collocations document seems pretty good to me. <a href="http://www.nltk.org/howto/collocations.html" rel="noreferrer">http://www.nltk.org/howto/collocations.html</a></p> <p>You need to give the scorer some actual sizable corpus to work with. Here is a working example using the Brown corpus built into NLTK. It takes about 30 seconds to run.</p> <pre><code>import nltk.collocations import nltk.corpus import collections bgm = nltk.collocations.BigramAssocMeasures() finder = nltk.collocations.BigramCollocationFinder.from_words( nltk.corpus.brown.words()) scored = finder.score_ngrams( bgm.likelihood_ratio ) # Group bigrams by first word in bigram. prefix_keys = collections.defaultdict(list) for key, scores in scored: prefix_keys[key[0]].append((key[1], scores)) # Sort keyed bigrams by strongest association. for key in prefix_keys: prefix_keys[key].sort(key = lambda x: -x[1]) print 'doctor', prefix_keys['doctor'][:5] print 'baseball', prefix_keys['baseball'][:5] print 'happy', prefix_keys['happy'][:5] </code></pre> <p>The output seems reasonable, works well for baseball, less so for doctor and happy.</p> <pre><code>doctor [('bills', 35.061321987405748), (',', 22.963930079491501), ('annoys', 19.009636692022365), ('had', 16.730384189212423), ('retorted', 15.190847940499127)] baseball [('game', 32.110754519752291), ('cap', 27.81891372457088), ('park', 23.509042621473505), ('games', 23.105033513054011), ("player's", 16.227872863424668)] happy [("''", 20.296341424483998), ('Spahn', 13.915820697905589), ('family', 13.734352182441569), (',', 13.55077617193821), ('bodybuilder', 13.513265447290536) </code></pre>
 

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