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	.. Copyright (C) 2001-2023 NLTK Project
	.. For license information, see LICENSE.TXT

	===================
	Dependency Grammars
	===================

	>>> from nltk.grammar import DependencyGrammar
	>>> from nltk.parse import (
	... DependencyGraph,
	... ProjectiveDependencyParser,
	... NonprojectiveDependencyParser,
	... )

	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 a custom cell extractor.

	>>> def custom_extractor(cells):
	... _, tag, head, rel = cells
	... return 'spam', 'spam', tag, tag, '', head, rel
	>>> dg = DependencyGraph(treebank_data, cell_extractor=custom_extractor)
	>>> dg.tree().pprint()
	(spam
	(spam spam spam (spam (spam spam)) spam)
	(spam (spam spam) (spam (spam spam spam)) (spam spam) spam))

	Custom cell extractors can take in and return an index.

	>>> def custom_extractor(cells, index):
	... word, tag, head, rel = cells
	... return (index, '{}-{}'.format(word, index), word,
	... tag, tag, '', head, rel)
	>>> dg = DependencyGraph(treebank_data, cell_extractor=custom_extractor)
	>>> dg.tree().pprint()
	(will-8
	(Vinken-2 Pierre-1 ,-3 (old-6 (years-5 61-4)) ,-7)
	(join-9
	(board-11 the-10)
	(as-12 (director-15 a-13 nonexecutive-14))
	(Nov.-16 29-17)
	.-18))

	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))
	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))

	Integration with MALT parser
	============================

	In case the top relation is different from the default, we can set it. In case
	of MALT parser, it's set to `'null'`.

	>>> dg_str = """1 I _ NN NN _ 2 nn _ _
	... 2 shot _ NN NN _ 0 null _ _
	... 3 an _ AT AT _ 2 dep _ _
	... 4 elephant _ NN NN _ 7 nn _ _
	... 5 in _ NN NN _ 7 nn _ _
	... 6 my _ NN NN _ 7 nn _ _
	... 7 pajamas _ NNS NNS _ 3 dobj _ _
	... """
	>>> dg = DependencyGraph(dg_str, top_relation_label='null')

	>>> len(dg.nodes)
	8

	>>> dg.root['word'], dg.root['address']
	('shot', 2)

	>>> print(dg.to_conll(10))
	1 I _ NN NN _ 2 nn _ _
	2 shot _ NN NN _ 0 null _ _
	3 an _ AT AT _ 2 dep _ _
	4 elephant _ NN NN _ 7 nn _ _
	5 in _ NN NN _ 7 nn _ _
	6 my _ NN NN _ 7 nn _ _
	7 pajamas _ NNS NNS _ 3 dobj _ _