python - NLTK 使用的实际例子

Question

我正在使用自然语言工具包（NLTK）。

它的文档（Book和HOWTO）非常庞大，并且示例有时稍微高级一些。

NLTK 的使用/应用有什么好的但基本的例子吗？我正在考虑诸如Stream Hacker博客上的NTLK 文章之类的事情。

Answer 1

这是我自己的实际示例，以方便其他人查找此问题（请原谅示例文本，这是我在Wikipedia上找到的第一件事）：

import nltk
import pprint

tokenizer = None
tagger = None

def init_nltk():
    global tokenizer
    global tagger
    tokenizer = nltk.tokenize.RegexpTokenizer(r'\w+|[^\w\s]+')
    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')]

Answer 2

NLP 通常非常有用，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 通过提取概念图来生成文件分类来帮助 MOSS 2010。它工作得非常好。文件开始以有用的方式聚集起来并不需要很长时间。

通常，要了解文本分析，您必须与您习惯的思维方式相切合。例如，文本分析对于发现非常有用。但是，大多数人甚至不知道搜索和发现之间的区别。如果您阅读了这些主题，您可能会“发现”您可能希望将 NLTK 用于工作的方式。

此外，请考虑您对没有 NLTK 的文本文件的世界观。您有一堆由空格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，例如句点（它也是小数点和缩写的后缀标记）。使用 NLTK，您可以获得单词，甚至更多地获得词性。现在您已经掌握了内容。使用 NLTK 发现文档中的概念和操作。使用 NLTK 来了解文档的“含义”。在这种情况下，含义是指文档中的本质关系。

对 NLTK 感到好奇是件好事。文本分析将在未来几年内大举突破。那些了解它的人将更适合更好地利用新机会。

Answer 3

我是streamhacker.com的作者（感谢您的提及，我从这个特定问题中获得了相当多的点击流量）。你具体想做什么？NLTK 有很多工具可以做各种各样的事情，但在使用这些工具的目的以及如何最好地使用它们方面缺乏明确的信息。它也面向学术问题，因此将教学示例转化为实际解决方案可能很繁重。