NLTK中用于解析的英语语法

>>> from stat_parser import Parser
>>> parser = Parser()
>>> print parser.parse(\"How can the net amount of entropy of the universe be massively decreased?\")
(SBARQ
  (WHADVP (WRB how))
  (SQ
    (MD can)
    (NP
      (NP (DT the) (JJ net) (NN amount))
      (PP
        (IN of)
        (NP
          (NP (NNS entropy))
          (PP (IN of) (NP (DT the) (NN universe))))))
    (VP (VB be) (ADJP (RB massively) (VBN decreased))))
  (. ?))

pip install spacy
python -m spacy.en.download all

from spacy.en import English
nlp = English()
doc = nlp(u\'A whole document.\\nNo preprocessing require.   Robust to arbitrary formating.\')
for sent in doc:
    for token in sent:
        if token.is_alpha:
            print token.orth_, token.tag_, token.head.lemma_

>>> from pattern.en import parse
>>>  
>>> s = \'The mobile web is more important than mobile apps.\'
>>> s = parse(s, relations=True, lemmata=True)
>>> print s

\'The/DT/B-NP/O/NP-SBJ-1/the mobile/JJ/I-NP/O/NP-SBJ-1/mobile\' ... 

nltk_data

nltk.download()

import nltk
parser = nltk.parse.malt.MaltParser()

text = word_tokenize(\"And now for something completely different\")
nltk.pos_tag(text)

[(\'And\', \'CC\'), (\'now\', \'RB\'), (\'for\', \'IN\'), (\'something\', \'NN\'),(\'completely\', \'RB\'), (\'different\', \'JJ\')]