Question

我遇到了一个严重问题：我已经下载了最新版本的NLTK 我得到了一个奇怪的POS输出：

import nltk
import re

sample_text="start please with me"
tokenized = nltk.sent_tokenize(sample_text)  

for i in tokenized:
            words=nltk.word_tokenize(i)
            tagged=nltk.pos_tag(words)
            chunkGram=r"""Chank___Start:{<VB|VBZ>*}  """                           
            chunkParser=nltk.RegexpParser(chunkGram)
            chunked=chunkParser.parse(tagged)
            print(chunked)

[OUT]：

（S start / JJ please / NN with / IN me / PRP）

我不知道为什么“start”被标记为JJ而“please”被标记为NN？

Answer 1

默认的NLTK pos_tag已经知道please是一个名词。在几乎任何情况下，这都不正确，例如。

>>> from nltk import pos_tag
>>> pos_tag('Please go away !'.split())
[('Please', 'NNP'), ('go', 'VB'), ('away', 'RB'), ('!', '.')]
>>> pos_tag('Please'.split())
[('Please', 'VB')]
>>> pos_tag('please'.split())
[('please', 'NN')]
>>> pos_tag('please !'.split())
[('please', 'NN'), ('!', '.')]
>>> pos_tag('Please !'.split())
[('Please', 'NN'), ('!', '.')]
>>> pos_tag('Would you please go away ?'.split())
[('Would', 'MD'), ('you', 'PRP'), ('please', 'VB'), ('go', 'VB'), ('away', 'RB'), ('?', '.')]
>>> pos_tag('Would you please go away !'.split())
[('Would', 'MD'), ('you', 'PRP'), ('please', 'VB'), ('go', 'VB'), ('away', 'RB'), ('!', '.')]
>>> pos_tag('Please go away ?'.split())
[('Please', 'NNP'), ('go', 'VB'), ('away', 'RB'), ('?', '.')]

使用WordNet作为基准，不应该存在please是名词的情况。

>>> from nltk.corpus import wordnet as wn
>>> wn.synsets('please')
[Synset('please.v.01'), Synset('please.v.02'), Synset('please.v.03'), Synset('please.r.01')]

但我认为这主要是由于用于训练PerceptronTagger的文本而不是标记器本身的实现。

现在，我们来看看预训练PerceptronTragger中的内容，我们发现它只知道1500多个单词：

>>> from nltk import PerceptronTagger
>>> tagger = PerceptronTagger()
>>> tagger.tagdict['I']
'PRP'
>>> tagger.tagdict['You']
'PRP'
>>> tagger.tagdict['start']
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
KeyError: 'start'
>>> tagger.tagdict['Start']
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
KeyError: 'Start'
>>> tagger.tagdict['please']
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
KeyError: 'please'
>>> tagger.tagdict['Please']
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
KeyError: 'Please'
>>> len(tagger.tagdict)
1549

你可以做的一件事是破解标记：

>>> tagger.tagdict['start'] = 'VB'
>>> tagger.tagdict['please'] = 'VB'
>>> tagger.tag('please start with me'.split())
[('please', 'VB'), ('start', 'VB'), ('with', 'IN'), ('me', 'PRP')]

但最合乎逻辑的做法是只需重新训练标记器，请参阅http://www.nltk.org/_modules/nltk/tag/perceptron.html#PerceptronTagger.train

如果您不想重新训练标记器，请参阅Python NLTK pos_tag not returning the correct part-of-speech tag

最有可能的是，使用StanfordPOSTagger可以获得所需内容：

>>> from nltk import StanfordPOSTagger
>>> sjar = '/home/alvas/stanford-postagger/stanford-postagger.jar'
>>> m = '/home/alvas/stanford-postagger/models/english-left3words-distsim.tagger'
>>> spos_tag = StanfordPOSTagger(m, sjar)
>>> spos_tag.tag('Please go away !'.split())
[(u'Please', u'VB'), (u'go', u'VB'), (u'away', u'RB'), (u'!', u'.')]
>>> spos_tag.tag('Please'.split())
[(u'Please', u'VB')]
>>> spos_tag.tag('Please !'.split())
[(u'Please', u'VB'), (u'!', u'.')]
>>> spos_tag.tag('please !'.split())
[(u'please', u'VB'), (u'!', u'.')]
>>> spos_tag.tag('please'.split())
[(u'please', u'VB')]
>>> spos_tag.tag('Would you please go away !'.split())
[(u'Would', u'MD'), (u'you', u'PRP'), (u'please', u'VB'), (u'go', u'VB'), (u'away', u'RB'), (u'!', u'.')]
>>> spos_tag.tag('Would you please go away ?'.split())
[(u'Would', u'MD'), (u'you', u'PRP'), (u'please', u'VB'), (u'go', u'VB'), (u'away', u'RB'), (u'?', u'.')]

对于Linux：请参阅https://gist.github.com/alvations/e1df0ba227e542955a8a

对于Windows：请参阅https://gist.github.com/alvations/0ed8641d7d2e1941b9f9

为什么NLTK中的pos_tag标记为“请”为NN？

1 个答案: