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242 lines
7.3 KiB
Plaintext
242 lines
7.3 KiB
Plaintext
.. Copyright (C) 2001-2019 NLTK Project
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.. For license information, see LICENSE.TXT
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===================
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Dependency Grammars
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===================
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>>> from nltk.grammar import DependencyGrammar
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>>> from nltk.parse import (
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... DependencyGraph,
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... ProjectiveDependencyParser,
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... NonprojectiveDependencyParser,
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... )
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CoNLL Data
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----------
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>>> treebank_data = """Pierre NNP 2 NMOD
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... Vinken NNP 8 SUB
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... , , 2 P
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... 61 CD 5 NMOD
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... years NNS 6 AMOD
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... old JJ 2 NMOD
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... , , 2 P
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... will MD 0 ROOT
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... join VB 8 VC
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... the DT 11 NMOD
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... board NN 9 OBJ
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... as IN 9 VMOD
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... a DT 15 NMOD
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... nonexecutive JJ 15 NMOD
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... director NN 12 PMOD
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... Nov. NNP 9 VMOD
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... 29 CD 16 NMOD
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... . . 9 VMOD
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... """
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>>> dg = DependencyGraph(treebank_data)
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>>> dg.tree().pprint()
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(will
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(Vinken Pierre , (old (years 61)) ,)
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(join (board the) (as (director a nonexecutive)) (Nov. 29) .))
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>>> for head, rel, dep in dg.triples():
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... print(
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... '({h[0]}, {h[1]}), {r}, ({d[0]}, {d[1]})'
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... .format(h=head, r=rel, d=dep)
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... )
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(will, MD), SUB, (Vinken, NNP)
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(Vinken, NNP), NMOD, (Pierre, NNP)
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(Vinken, NNP), P, (,, ,)
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(Vinken, NNP), NMOD, (old, JJ)
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(old, JJ), AMOD, (years, NNS)
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(years, NNS), NMOD, (61, CD)
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(Vinken, NNP), P, (,, ,)
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(will, MD), VC, (join, VB)
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(join, VB), OBJ, (board, NN)
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(board, NN), NMOD, (the, DT)
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(join, VB), VMOD, (as, IN)
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(as, IN), PMOD, (director, NN)
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(director, NN), NMOD, (a, DT)
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(director, NN), NMOD, (nonexecutive, JJ)
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(join, VB), VMOD, (Nov., NNP)
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(Nov., NNP), NMOD, (29, CD)
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(join, VB), VMOD, (., .)
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Using a custom cell extractor.
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>>> def custom_extractor(cells):
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... _, tag, head, rel = cells
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... return 'spam', 'spam', tag, tag, '', head, rel
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>>> dg = DependencyGraph(treebank_data, cell_extractor=custom_extractor)
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>>> dg.tree().pprint()
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(spam
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(spam spam spam (spam (spam spam)) spam)
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(spam (spam spam) (spam (spam spam spam)) (spam spam) spam))
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Custom cell extractors can take in and return an index.
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>>> def custom_extractor(cells, index):
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... word, tag, head, rel = cells
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... return (index, '{}-{}'.format(word, index), word,
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... tag, tag, '', head, rel)
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>>> dg = DependencyGraph(treebank_data, cell_extractor=custom_extractor)
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>>> dg.tree().pprint()
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(will-8
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(Vinken-2 Pierre-1 ,-3 (old-6 (years-5 61-4)) ,-7)
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(join-9
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(board-11 the-10)
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(as-12 (director-15 a-13 nonexecutive-14))
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(Nov.-16 29-17)
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.-18))
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Using the dependency-parsed version of the Penn Treebank corpus sample.
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>>> from nltk.corpus import dependency_treebank
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>>> t = dependency_treebank.parsed_sents()[0]
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>>> print(t.to_conll(3)) # doctest: +NORMALIZE_WHITESPACE
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Pierre NNP 2
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Vinken NNP 8
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, , 2
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61 CD 5
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years NNS 6
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old JJ 2
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, , 2
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will MD 0
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join VB 8
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the DT 11
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board NN 9
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as IN 9
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a DT 15
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nonexecutive JJ 15
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director NN 12
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Nov. NNP 9
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29 CD 16
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. . 8
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Using the output of zpar (like Malt-TAB but with zero-based indexing)
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>>> zpar_data = """
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... Pierre NNP 1 NMOD
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... Vinken NNP 7 SUB
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... , , 1 P
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... 61 CD 4 NMOD
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... years NNS 5 AMOD
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... old JJ 1 NMOD
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... , , 1 P
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... will MD -1 ROOT
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... join VB 7 VC
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... the DT 10 NMOD
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... board NN 8 OBJ
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... as IN 8 VMOD
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... a DT 14 NMOD
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... nonexecutive JJ 14 NMOD
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... director NN 11 PMOD
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... Nov. NNP 8 VMOD
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... 29 CD 15 NMOD
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... . . 7 P
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... """
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>>> zdg = DependencyGraph(zpar_data, zero_based=True)
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>>> print(zdg.tree())
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(will
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(Vinken Pierre , (old (years 61)) ,)
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(join (board the) (as (director a nonexecutive)) (Nov. 29))
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.)
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Projective Dependency Parsing
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-----------------------------
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>>> grammar = DependencyGrammar.fromstring("""
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... 'fell' -> 'price' | 'stock'
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... 'price' -> 'of' 'the'
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... 'of' -> 'stock'
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... 'stock' -> 'the'
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... """)
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>>> print(grammar)
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Dependency grammar with 5 productions
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'fell' -> 'price'
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'fell' -> 'stock'
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'price' -> 'of' 'the'
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'of' -> 'stock'
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'stock' -> 'the'
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>>> dp = ProjectiveDependencyParser(grammar)
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>>> for t in sorted(dp.parse(['the', 'price', 'of', 'the', 'stock', 'fell'])):
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... print(t)
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(fell (price the (of (stock the))))
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(fell (price the of) (stock the))
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(fell (price the of the) stock)
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Non-Projective Dependency Parsing
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---------------------------------
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>>> grammar = DependencyGrammar.fromstring("""
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... 'taught' -> 'play' | 'man'
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... 'man' -> 'the'
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... 'play' -> 'golf' | 'dog' | 'to'
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... 'dog' -> 'his'
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... """)
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>>> print(grammar)
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Dependency grammar with 7 productions
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'taught' -> 'play'
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'taught' -> 'man'
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'man' -> 'the'
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'play' -> 'golf'
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'play' -> 'dog'
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'play' -> 'to'
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'dog' -> 'his'
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>>> dp = NonprojectiveDependencyParser(grammar)
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>>> g, = dp.parse(['the', 'man', 'taught', 'his', 'dog', 'to', 'play', 'golf'])
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>>> print(g.root['word'])
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taught
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>>> for _, node in sorted(g.nodes.items()):
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... if node['word'] is not None:
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... print('{address} {word}: {d}'.format(d=node['deps'][''], **node))
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1 the: []
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2 man: [1]
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3 taught: [2, 7]
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4 his: []
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5 dog: [4]
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6 to: []
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7 play: [5, 6, 8]
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8 golf: []
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>>> print(g.tree())
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(taught (man the) (play (dog his) to golf))
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Integration with MALT parser
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============================
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In case the top relation is different from the default, we can set it. In case
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of MALT parser, it's set to `'null'`.
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>>> dg_str = """1 I _ NN NN _ 2 nn _ _
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... 2 shot _ NN NN _ 0 null _ _
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... 3 an _ AT AT _ 2 dep _ _
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... 4 elephant _ NN NN _ 7 nn _ _
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... 5 in _ NN NN _ 7 nn _ _
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... 6 my _ NN NN _ 7 nn _ _
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... 7 pajamas _ NNS NNS _ 3 dobj _ _
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... """
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>>> dg = DependencyGraph(dg_str, top_relation_label='null')
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>>> len(dg.nodes)
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8
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>>> dg.root['word'], dg.root['address']
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('shot', 2)
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>>> print(dg.to_conll(10)) # doctest: +NORMALIZE_WHITESPACE
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1 I _ NN NN _ 2 nn _ _
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2 shot _ NN NN _ 0 null _ _
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3 an _ AT AT _ 2 dep _ _
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4 elephant _ NN NN _ 7 nn _ _
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5 in _ NN NN _ 7 nn _ _
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6 my _ NN NN _ 7 nn _ _
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7 pajamas _ NNS NNS _ 3 dobj _ _
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