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.googlepre.com/files/hunpos-1.0-linux.tgz$ tar zxvf hunpos-1.0-linux.tgz$ wget https://hunpos.googlepre.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/
