NLTK使用的实例
我正在玩弄自然语言工具包（NLTK）。
它的文档（Book和HOWTO）非常庞大，有时候这些例子稍有进步。
NLTK的使用/应用有没有很好但基本的例子？ 我正在考虑像Stream Hacker博客上的NTLK文章。
这里有我自己的实例来帮助其他任何人看到这个问题（原谅示例文本，这是我在Wikipedia上发现的第一件事）：
import nltk
import pprint
tokenizer = None
tagger = None
def init_nltk():
global tokenizer
global tagger
tokenizer = nltk.tokenize.RegexpTokenizer(r'w+|[^ws]+')
tagger = nltk.UnigramTagger(nltk.corpus.brown.tagged_sents())
def tag(text):
global tokenizer
global tagger
if not tokenizer:
init_nltk()
tokenized = tokenizer.tokenize(text)
tagged = tagger.tag(tokenized)
tagged.sort(lambda x,y:cmp(x[1],y[1]))
return tagged
def main():
text = """Mr Blobby is a fictional character who featured on Noel
Edmonds' Saturday night entertainment show Noel's House Party,
which was often a ratings winner in the 1990s. Mr Blobby also
appeared on the Jamie Rose show of 1997. He was designed as an
outrageously over the top parody of a one-dimensional, mute novelty
character, which ironically made him distinctive, absurd and popular.
He was a large pink humanoid, covered with yellow spots, sporting a
permanent toothy grin and jiggling eyes. He communicated by saying
the word "blobby" in an electronically-altered voice, expressing
his moods through tone of voice and repetition.
There was a Mrs. Blobby, seen briefly in the video, and sold as a
doll.
However Mr Blobby actually started out as part of the 'Gotcha'
feature during the show's second series (originally called 'Gotcha
Oscars' until the threat of legal action from the Academy of Motion
Picture Arts and Sciences[citation needed]), in which celebrities
were caught out in a Candid Camera style prank. Celebrities such as
dancer Wayne Sleep and rugby union player Will Carling would be
enticed to take part in a fictitious children's programme based around
their profession. Mr Blobby would clumsily take part in the activity,
knocking over the set, causing mayhem and saying "blobby blobby
blobby", until finally when the prank was revealed, the Blobby
costume would be opened - revealing Noel inside. This was all the more
surprising for the "victim" as during rehearsals Blobby would be
played by an actor wearing only the arms and legs of the costume and
speaking in a normal manner.[citation needed]"""
tagged = tag(text)
l = list(set(tagged))
l.sort(lambda x,y:cmp(x[1],y[1]))
pprint.pprint(l)
if __name__ == '__main__':
main()
输出：
[('rugby', None),
('Oscars', None),
('1990s', None),
('",', None),
('Candid', None),
('"', None),
('blobby', None),
('Edmonds', None),
('Mr', None),
('outrageously', None),
('.[', None),
('toothy', None),
('Celebrities', None),
('Gotcha', None),
(']),', None),
('Jamie', None),
('humanoid', None),
('Blobby', None),
('Carling', None),
('enticed', None),
('programme', None),
('1997', None),
('s', None),
("'", "'"),
('[', '('),
('(', '('),
(']', ')'),
(',', ','),
('.', '.'),
('all', 'ABN'),
('the', 'AT'),
('an', 'AT'),
('a', 'AT'),
('be', 'BE'),
('were', 'BED'),
('was', 'BEDZ'),
('is', 'BEZ'),
('and', 'CC'),
('one', 'CD'),
('until', 'CS'),
('as', 'CS'),
('This', 'DT'),
('There', 'EX'),
('of', 'IN'),
('inside', 'IN'),
('from', 'IN'),
('around', 'IN'),
('with', 'IN'),
('through', 'IN'),
('-', 'IN'),
('on', 'IN'),
('in', 'IN'),
('by', 'IN'),
('during', 'IN'),
('over', 'IN'),
('for', 'IN'),
('distinctive', 'JJ'),
('permanent', 'JJ'),
('mute', 'JJ'),
('popular', 'JJ'),
('such', 'JJ'),
('fictional', 'JJ'),
('yellow', 'JJ'),
('pink', 'JJ'),
('fictitious', 'JJ'),
('normal', 'JJ'),
('dimensional', 'JJ'),
('legal', 'JJ'),
('large', 'JJ'),
('surprising', 'JJ'),
('absurd', 'JJ'),
('Will', 'MD'),
('would', 'MD'),
('style', 'NN'),
('threat', 'NN'),
('novelty', 'NN'),
('union', 'NN'),
('prank', 'NN'),
('winner', 'NN'),
('parody', 'NN'),
('player', 'NN'),
('actor', 'NN'),
('character', 'NN'),
('victim', 'NN'),
('costume', 'NN'),
('action', 'NN'),
('activity', 'NN'),
('dancer', 'NN'),
('grin', 'NN'),
('doll', 'NN'),
('top', 'NN'),
('mayhem', 'NN'),
('citation', 'NN'),
('part', 'NN'),
('repetition', 'NN'),
('manner', 'NN'),
('tone', 'NN'),
('Picture', 'NN'),
('entertainment', 'NN'),
('night', 'NN'),
('series', 'NN'),
('voice', 'NN'),
('Mrs', 'NN'),
('video', 'NN'),
('Motion', 'NN'),
('profession', 'NN'),
('feature', 'NN'),
('word', 'NN'),
('Academy', 'NN-TL'),
('Camera', 'NN-TL'),
('Party', 'NN-TL'),
('House', 'NN-TL'),
('eyes', 'NNS'),
('spots', 'NNS'),
('rehearsals', 'NNS'),
('ratings', 'NNS'),
('arms', 'NNS'),
('celebrities', 'NNS'),
('children', 'NNS'),
('moods', 'NNS'),
('legs', 'NNS'),
('Sciences', 'NNS-TL'),
('Arts', 'NNS-TL'),
('Wayne', 'NP'),
('Rose', 'NP'),
('Noel', 'NP'),
('Saturday', 'NR'),
('second', 'OD'),
('his', 'PP$'),
('their', 'PP$'),
('him', 'PPO'),
('He', 'PPS'),
('more', 'QL'),
('However', 'RB'),
('actually', 'RB'),
('also', 'RB'),
('clumsily', 'RB'),
('originally', 'RB'),
('only', 'RB'),
('often', 'RB'),
('ironically', 'RB'),
('briefly', 'RB'),
('finally', 'RB'),
('electronically', 'RB-HL'),
('out', 'RP'),
('to', 'TO'),
('show', 'VB'),
('Sleep', 'VB'),
('take', 'VB'),
('opened', 'VBD'),
('played', 'VBD'),
('caught', 'VBD'),
('appeared', 'VBD'),
('revealed', 'VBD'),
('started', 'VBD'),
('saying', 'VBG'),
('causing', 'VBG'),
('expressing', 'VBG'),
('knocking', 'VBG'),
('wearing', 'VBG'),
('speaking', 'VBG'),
('sporting', 'VBG'),
('revealing', 'VBG'),
('jiggling', 'VBG'),
('sold', 'VBN'),
('called', 'VBN'),
('made', 'VBN'),
('altered', 'VBN'),
('based', 'VBN'),
('designed', 'VBN'),
('covered', 'VBN'),
('communicated', 'VBN'),
('needed', 'VBN'),
('seen', 'VBN'),
('set', 'VBN'),
('featured', 'VBN'),
('which', 'WDT'),
('who', 'WPS'),
('when', 'WRB')]
一般而言，NLP非常有用，因此您可能希望将搜索范围扩大到文本分析的一般应用。 我使用NLTK通过提取概念图生成文件分类来帮助MOSS 2010。 它工作得很好。 文件开始以有用的方式进行聚类不需要很长时间。
通常情况下，要理解文本分析，您必须考虑切线方式以及您习惯思考的方式。 例如，文本分析对发现非常有用。 然而，大多数人甚至都不知道搜索和发现之间的区别。 如果你阅读了这些主题，你可能会“发现”你想要把NLTK工作的方式。
另外，考虑你的世界观的文本文件没有NLTK。 你有一堆由空白和标点符号分隔的随机长度字符串。 一些标点符号改变了它的使用方式，例如句号（缩写也是一个小数点和一个后缀标记）。使用NLTK，您可以获得更多单词，并且可以获得更多词性。 现在你掌握了内容。 使用NLTK来发现文档中的概念和操作。 使用NLTK来获取文档的“含义”。 在这种情况下的含义是指文件中的重要关系。
对NLTK感到好奇是件好事。 文字分析将在未来几年内大规模地突围。 那些了解它的人会更适合更好地利用新的机会。
我是streamhacker.com的作者（感谢您提及，我从这个特定问题中获得了相当多的点击流量）。 你想要做什么？ NLTK有很多工具可以用来做各种事情，但是在某种程度上缺乏有关如何使用这些工具以及如何最好地使用这些工具的明确信息。 它也是面向学术问题的，因此将教学范例翻译成实际解决方案可能会很繁重。
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