CoNLL Data

>>> treebank_data = """Pierre  NNP     2       NMOD
... Vinken  NNP     8       SUB
... ,       ,       2       P
... 61      CD      5       NMOD
... years   NNS     6       AMOD
... old     JJ      2       NMOD
... ,       ,       2       P
... will    MD      0       ROOT
... join    VB      8       VC
... the     DT      11      NMOD
... board   NN      9       OBJ
... as      IN      9       VMOD
... a       DT      15      NMOD
... nonexecutive    JJ      15      NMOD
... director        NN      12      PMOD
... Nov.    NNP     9       VMOD
... 29      CD      16      NMOD
... .       .       9       VMOD
... """

>>> dg = DependencyGraph(treebank_data)
>>> dg.tree().pprint()
(will
  (Vinken Pierre , (old (years 61)) ,)
  (join (board the) (as (director a nonexecutive)) (Nov. 29) .))
>>> for head, rel, dep in dg.triples():
...     print(
...         '({h[0]}, {h[1]}), {r}, ({d[0]}, {d[1]})'
...         .format(h=head, r=rel, d=dep)
...     )
(will, MD), SUB, (Vinken, NNP)
(Vinken, NNP), NMOD, (Pierre, NNP)
(Vinken, NNP), P, (,, ,)
(Vinken, NNP), NMOD, (old, JJ)
(old, JJ), AMOD, (years, NNS)
(years, NNS), NMOD, (61, CD)
(Vinken, NNP), P, (,, ,)
(will, MD), VC, (join, VB)
(join, VB), OBJ, (board, NN)
(board, NN), NMOD, (the, DT)
(join, VB), VMOD, (as, IN)
(as, IN), PMOD, (director, NN)
(director, NN), NMOD, (a, DT)
(director, NN), NMOD, (nonexecutive, JJ)
(join, VB), VMOD, (Nov., NNP)
(Nov., NNP), NMOD, (29, CD)
(join, VB), VMOD, (., .)

Using the dependency-parsed version of the Penn Treebank corpus sample.

>>> from nltk.corpus import dependency_treebank
>>> t = dependency_treebank.parsed_sents()[0]
>>> print(t.to_conll(3))  # doctest: +NORMALIZE_WHITESPACE
Pierre      NNP     2
Vinken      NNP     8
,   ,       2
61  CD      5
years       NNS     6
old JJ      2
,   ,       2
will        MD      0
join        VB      8
the DT      11
board       NN      9
as  IN      9
a   DT      15
nonexecutive        JJ      15
director    NN      12
Nov.        NNP     9
29  CD      16
.   .       8

Using the output of zpar (like Malt-TAB but with zero-based indexing)

>>> zpar_data = """
... Pierre  NNP     1       NMOD
... Vinken  NNP     7       SUB
... ,       ,       1       P
... 61      CD      4       NMOD
... years   NNS     5       AMOD
... old     JJ      1       NMOD
... ,       ,       1       P
... will    MD      -1      ROOT
... join    VB      7       VC
... the     DT      10      NMOD
... board   NN      8       OBJ
... as      IN      8       VMOD
... a       DT      14      NMOD
... nonexecutive    JJ      14      NMOD
... director        NN      11      PMOD
... Nov.    NNP     8       VMOD
... 29      CD      15      NMOD
... .       .       7       P
... """

>>> zdg = DependencyGraph(zpar_data, zero_based=True)
>>> print(zdg.tree())
(will
  (Vinken Pierre , (old (years 61)) ,)
  (join (board the) (as (director a nonexecutive)) (Nov. 29))
  .)

Projective Dependency Parsing

>>> grammar = DependencyGrammar.fromstring("""
... 'fell' -> 'price' | 'stock'
... 'price' -> 'of' 'the'
... 'of' -> 'stock'
... 'stock' -> 'the'
... """)
>>> print(grammar)
Dependency grammar with 5 productions
  'fell' -> 'price'
  'fell' -> 'stock'
  'price' -> 'of' 'the'
  'of' -> 'stock'
  'stock' -> 'the'

>>> dp = ProjectiveDependencyParser(grammar)
>>> for t in sorted(dp.parse(['the', 'price', 'of', 'the', 'stock', 'fell'])):
...     print(t)
(fell (price the (of (stock the))))
(fell (price the of) (stock the))
(fell (price the of the) stock)

Non-Projective Dependency Parsing

>>> grammar = DependencyGrammar.fromstring("""
... 'taught' -> 'play' | 'man'
... 'man' -> 'the'
... 'play' -> 'golf' | 'dog' | 'to'
... 'dog' -> 'his'
... """)
>>> print(grammar)
Dependency grammar with 7 productions
  'taught' -> 'play'
  'taught' -> 'man'
  'man' -> 'the'
  'play' -> 'golf'
  'play' -> 'dog'
  'play' -> 'to'
  'dog' -> 'his'

>>> dp = NonprojectiveDependencyParser(grammar)
>>> g, = dp.parse(['the', 'man', 'taught', 'his', 'dog', 'to', 'play', 'golf'])

>>> print(g.root['word'])
taught

>>> for _, node in sorted(g.nodes.items()):
...     if node['word'] is not None:
...         print('{address} {word}: {d}'.format(d=node['deps'][''], **node))
1 the: []
2 man: [1]
3 taught: [2, 7]
4 his: []
5 dog: [4]
6 to: []
7 play: [5, 6, 8]
8 golf: []

>>> print(g.tree())
(taught (man the) (play (dog his) to golf))