English grammar for parsing in NLTK

You can take a look at pyStatParser, a simple python statistical parser that returns NLTK parse Trees. It comes with public treebanks and it generates the grammar model only the first time you instantiate a Parser object (in about 8 seconds). It uses a CKY algorithm and it parses average length sentences (like the one below) in under a second.

>>> 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))))
  (. ?))

My library, spaCy, provides a high performance dependency parser.

Installation:

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

Usage:

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_

Choi et al. (2015) found spaCy to be the fastest dependency parser available. It processes over 13,000 sentences a second, on a single thread. On the standard WSJ evaluation it scores 92.7%, over 1% more accurate than any of CoreNLP's models.

There are a few grammars in the nltk_data distribution. In your Python interpreter, issue nltk.download().

There is a Library called Pattern. It is quite fast and easy to use.

>>> 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' ...