Python NLTK pos_tag没有返回正确的词性标签

Question

有这个：

text = word_tokenize("The quick brown fox jumps over the lazy dog")

跑步：

nltk.pos_tag(text)

我明白了：

[('The', 'DT'), ('quick', 'NN'), ('brown', 'NN'), ('fox', 'NN'), ('jumps', 'NNS'), ('over', 'IN'), ('the', 'DT'), ('lazy', 'NN'), ('dog', 'NN')]

这是不正确的。句子中quick brown lazy的标签应为：

('quick', 'JJ'), ('brown', 'JJ') , ('lazy', 'JJ')

通过他们的online tool进行测试会得到相同的结果; quick，brown和fox应该是形容词而不是名词。

Answer 1

简而言之：

  

NLTK并不完美。事实上，没有任何模型是完美的。

注意：

自NLTK 3.1版起，默认pos_tag功能不再是old MaxEnt English pickle。

现在是来自@Honnibal's implementation的 perceptron标记，请参阅nltk.tag.pos_tag

>>> import inspect
>>> print inspect.getsource(pos_tag)
def pos_tag(tokens, tagset=None):
    tagger = PerceptronTagger()
    return _pos_tag(tokens, tagset, tagger) 

还是它更好但不完美：

>>> from nltk import pos_tag
>>> pos_tag("The quick brown fox jumps over the lazy dog".split())
[('The', 'DT'), ('quick', 'JJ'), ('brown', 'NN'), ('fox', 'NN'), ('jumps', 'VBZ'), ('over', 'IN'), ('the', 'DT'), ('lazy', 'JJ'), ('dog', 'NN')]

在某些时候，如果有人想要TL;DR解决方案，请参阅https://github.com/alvations/nltk_cli

长：

尝试使用其他标记器（请参阅https://github.com/nltk/nltk/tree/develop/nltk/tag），例如：

• HunPos
• Stanford POS
• 塞纳

使用NLTK的默认MaxEnt POS标记器，即nltk.pos_tag ：

>>> from nltk import word_tokenize, pos_tag
>>> text = "The quick brown fox jumps over the lazy dog"
>>> pos_tag(word_tokenize(text))
[('The', 'DT'), ('quick', 'NN'), ('brown', 'NN'), ('fox', 'NN'), ('jumps', 'NNS'), ('over', 'IN'), ('the', 'DT'), ('lazy', 'NN'), ('dog', 'NN')]

使用Stanford POS tagger ：

$ cd ~
$ wget http://nlp.stanford.edu/software/stanford-postagger-2015-04-20.zip
$ unzip stanford-postagger-2015-04-20.zip
$ mv stanford-postagger-2015-04-20 stanford-postagger
$ python
>>> from os.path import expanduser
>>> home = expanduser("~")
>>> from nltk.tag.stanford import POSTagger
>>> _path_to_model = home + '/stanford-postagger/models/english-bidirectional-distsim.tagger'
>>> _path_to_jar = home + '/stanford-postagger/stanford-postagger.jar'
>>> st = POSTagger(path_to_model=_path_to_model, path_to_jar=_path_to_jar)
>>> text = "The quick brown fox jumps over the lazy dog"
>>> st.tag(text.split())
[(u'The', u'DT'), (u'quick', u'JJ'), (u'brown', u'JJ'), (u'fox', u'NN'), (u'jumps', u'VBZ'), (u'over', u'IN'), (u'the', u'DT'), (u'lazy', u'JJ'), (u'dog', u'NN')]

使用HunPOS （注意：默认编码为ISO-8859-1而非UTF8）：

$ cd ~
$ wget https://hunpos.googlecode.com/files/hunpos-1.0-linux.tgz
$ tar zxvf hunpos-1.0-linux.tgz
$ wget https://hunpos.googlecode.com/files/en_wsj.model.gz
$ gzip -d en_wsj.model.gz 
$ mv en_wsj.model hunpos-1.0-linux/
$ python
>>> from os.path import expanduser
>>> home = expanduser("~")
>>> from nltk.tag.hunpos import HunposTagger
>>> _path_to_bin = home + '/hunpos-1.0-linux/hunpos-tag'
>>> _path_to_model = home + '/hunpos-1.0-linux/en_wsj.model'
>>> ht = HunposTagger(path_to_model=_path_to_model, path_to_bin=_path_to_bin)
>>> text = "The quick brown fox jumps over the lazy dog"
>>> ht.tag(text.split())
[('The', 'DT'), ('quick', 'JJ'), ('brown', 'JJ'), ('fox', 'NN'), ('jumps', 'NNS'), ('over', 'IN'), ('the', 'DT'), ('lazy', 'JJ'), ('dog', 'NN')]

使用Senna （确保您拥有最新版本的NLTK，对API进行了一些更改）：

$ cd ~
$ wget http://ronan.collobert.com/senna/senna-v3.0.tgz
$ tar zxvf senna-v3.0.tgz
$ python
>>> from os.path import expanduser
>>> home = expanduser("~")
>>> from nltk.tag.senna import SennaTagger
>>> st = SennaTagger(home+'/senna')
>>> text = "The quick brown fox jumps over the lazy dog"
>>> st.tag(text.split())
[('The', u'DT'), ('quick', u'JJ'), ('brown', u'JJ'), ('fox', u'NN'), ('jumps', u'VBZ'), ('over', u'IN'), ('the', u'DT'), ('lazy', u'JJ'), ('dog', u'NN')]

或尝试构建更好的POS标记：

• Ngram Tagger：http://streamhacker.com/2008/11/03/part-of-speech-tagging-with-nltk-part-1/
• Affix / Regex Tagger：http://streamhacker.com/2008/11/10/part-of-speech-tagging-with-nltk-part-2/
• 构建自己的Brill（阅读代码，它是一个非常有趣的标记器，http://www.nltk.org/_modules/nltk/tag/brill.html），请参阅http://streamhacker.com/2008/12/03/part-of-speech-tagging-with-nltk-part-3/
• Perceptron Tagger：https://honnibal.wordpress.com/2013/09/11/a-good-part-of-speechpos-tagger-in-about-200-lines-of-python/
• LDA Tagger：http://scm.io/blog/hack/2015/02/lda-intentions/

抱怨堆栈溢出的pos_tag准确度包括：

• POS tagging - NLTK thinks noun is adjective
• python NLTK POS tagger not behaving as expected
• How to obtain better results using NLTK pos tag
• pos_tag in NLTK does not tag sentences correctly

关于NLTK HunPos的问题包括：

• How do I tag textfiles with hunpos in nltk?
• Does anyone know how to configure the hunpos wrapper class on nltk?

NLTK和Stanford POS标记器的问题包括：

• trouble importing stanford pos tagger into nltk
• Java Command Fails in NLTK Stanford POS Tagger
• Error using Stanford POS Tagger in NLTK Python
• How to improve speed with Stanford NLP Tagger and NLTK
• Nltk stanford pos tagger error : Java command failed
• Instantiating and using StanfordTagger within NLTK
• Running Stanford POS tagger in NLTK leads to "not a valid Win32 application" on Windows

Answer 2

诸如更换为Stanford或Senna或HunPOS标记器的解决方案肯定会产生结果，但是这是一种更简单的方法来尝试也包含在NLTK中的不同标记器。

目前，NTLK中的默认POS标记器是平均感知器标记器。这是一个选择使用Maxent Treebank Tagger的函数：

def treebankTag(text)
    words = nltk.word_tokenize(text)
    treebankTagger = nltk.data.load('taggers/maxent_treebank_pos_tagger/english.pickle')
    return treebankTagger.tag(words)

我发现NLTK中的平均感知器预训练标签器偏向于将某些形容词视为名词，如您的示例。树堤标记器为我提供了更多正确的形容词。

Answer 3

def tagPOS(textcontent, taggedtextcontent, defined_tags):
    # Write your code here
    token = nltk.word_tokenize(textcontent)
    nltk_pos_tags = nltk.pos_tag(token)
    
    unigram_pos_tag = nltk.UnigramTagger(model=defined_tags).tag(token)
    
    tagged_pos_tag = [ nltk.tag.str2tuple(word) for word in taggedtextcontent.split() ]
    
    return (nltk_pos_tags,tagged_pos_tag,unigram_pos_tag)