Python NLTK pos_tag未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")

并运行：

nltk.pos_tag(text)

我得到：

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

这是不正确的。quick brown lazy句子中的标签应为：

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

通过他们的在线工具测试得出相同的结果。quick，brown并且fox应该是形容词而不是名词。

Answer 1

简而言之 ：

NLTK并不完美。实际上，没有任何模型是完美的。

注意：

从NLTK版本3.1开始，默认pos_tag功能不再是旧的MaxEnt English pickle。

现在它是 感知恶搞 从@
Honnibal的实现
，见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)

还是更好，但并不完美：

>>> 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，请参见https://github.com/alvations/nltk_cli

总而言之 ：

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

• Hanbao
  Stanford POS
  Senna leaf
  

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')]

使用斯坦福POS标记器 ：

$ 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而不是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 （请确保您拥有最新版本的NLTK，并对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')]

或者尝试构建更好的POS标记器 ：

• 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，这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/使用nltk-part-3进行语音标记的部分/
• 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/