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|
# Natural Language Toolkit: Dependency Grammars
#
# Copyright (C) 2001-2009 NLTK Project
# Author: Jason Narad <jason.narad@gmail.com>
# Steven Bird <sb@csse.unimelb.edu.au> (modifications)
#
# URL: <http://www.nltk.org/>
# For license information, see LICENSE.TXT
#
"""
Tools for reading and writing dependency trees.
The input is assumed to be in U{Malt-TAB<http://w3.msi.vxu.se/~nivre/research/MaltXML.html>} format.
Currently only reads the first tree in a file.
"""
from nltk import Tree
from pprint import pformat
import re
#################################################################
# DependencyGraph Class
#################################################################
class DependencyGraph(object):
"""
A container for the nodes and labelled edges of a dependency structure.
"""
def __init__(self, tree_str=None):
"""
We place a dummy 'top' node in the first position
in the nodelist, since the root node is often assigned '0'
as its head. This also means that the indexing of the nodelist
corresponds directly to the Malt-TAB format, which starts at 1.
"""
top = {'word':None, 'deps':[], 'rel': 'TOP', 'tag': 'TOP', 'address': 0}
self.nodelist = [top]
self.root = None
self.stream = None
if tree_str:
self._parse(tree_str)
def remove_by_address(self, address):
"""
Removes the node with the given address. References
to this node in others will still exist.
"""
node_index = len(self.nodelist) - 1
while(node_index >= 0):
node = self.nodelist[node_index]
if node['address'] == address:
self.nodelist.pop(node_index)
node_index -= 1
def redirect_arcs(self, originals, redirect):
"""
Redirects arcs to any of the nodes in the originals list
to the redirect node address.
"""
for node in self.nodelist:
new_deps = []
for dep in node['deps']:
if dep in originals:
new_deps.append(redirect)
else:
new_deps.append(dep)
node['deps'] = new_deps
def add_arc(self, head_address, mod_address):
"""
Adds an arc from the node specified by head_address to the
node specified by the mod address.
"""
for node in self.nodelist:
if node['address'] == head_address and (mod_address not in node['deps']):
node['deps'].append(mod_address)
def connect_graph(self):
"""
Fully connects all non-root nodes. All nodes are set to be dependents
of the root node.
"""
for node1 in self.nodelist:
for node2 in self.nodelist:
if node1['address'] != node2['address'] and node2['rel'] != 'TOP':
node1['deps'].append(node2['address'])
# fix error and return
def get_by_address(self, node_address):
"""
Returns the node with the given address.
"""
for node in self.nodelist:
if node['address'] == node_address:
return node
print 'THROW ERROR: address not found in -get_by_address-'
return -1
def contains_address(self, node_address):
"""
Returns true if the graph contains a node with the given node
address, false otherwise.
"""
for node in self.nodelist:
if node['address'] == node_address:
return True
return False
def __str__(self):
return pformat(self.nodelist)
def __repr__(self):
return "<DependencyGraph with %d nodes>" % len(self.nodelist)
@staticmethod
def load(file):
"""
@param file: a file in Malt-TAB format
"""
return DependencyGraph(open(file).read())
@staticmethod
def _normalize(line):
"""
Deal with lines in which spaces are used rather than tabs.
"""
SPC = re.compile(' +')
return re.sub(SPC, '\t', line).strip()
def left_children(self, node_index):
"""
Returns the number of left children under the node specified
by the given address.
"""
children = self.nodelist[node_index]['deps']
index = self.nodelist[node_index]['address']
return sum(1 for c in children if c < index)
def right_children(self, node_index):
"""
Returns the number of right children under the node specified
by the given address.
"""
children = self.nodelist[node_index]['deps']
index = self.nodelist[node_index]['address']
return sum(1 for c in children if c > index)
def add_node(self, node):
if not self.contains_address(node['address']):
self.nodelist.append(node)
def _parse(self, input):
lines = [DependencyGraph._normalize(line) for line in input.split('\n') if line.strip()]
temp = []
for index, line in enumerate(lines):
# print line
try:
cells = line.split('\t')
nrCells = len(cells)
if nrCells == 3:
word, tag, head = cells
rel = ''
elif nrCells == 4:
word, tag, head, rel = cells
elif nrCells == 10:
_, word, _, _, tag, _, head, rel, _, _ = cells
else:
raise ValueError('Number of tab-delimited fields (%d) not supported by CoNLL(10) or Malt-Tab(4) format' % (nrCells))
head = int(head)
self.nodelist.append({'address': index+1, 'word': word, 'tag': tag,
'head': head, 'rel': rel,
'deps': [d for (d,h) in temp if h == index+1]})
try:
self.nodelist[head]['deps'].append(index+1)
except IndexError:
temp.append((index+1, head))
except ValueError:
break
root_address = self.nodelist[0]['deps'][0]
self.root = self.nodelist[root_address]
def _word(self, node, filter=True):
w = node['word']
if filter:
if w != ',': return w
return w
def _tree(self, i):
"""
Recursive function for turning dependency graphs into
NLTK trees.
@type i: C{int}
@param i: index of a node in C{nodelist}
@return: either a word (if the indexed node
is a leaf) or a L{Tree}.
"""
node = self.nodelist[i]
word = node['word']
deps = node['deps']
if len(deps) == 0:
return word
else:
return Tree(word, [self._tree(j) for j in deps])
def tree(self):
"""
Starting with the C{root} node, build a dependency tree using the NLTK
L{Tree} constructor. Dependency labels are omitted.
"""
node = self.root
word = node['word']
deps = node['deps']
return Tree(word, [self._tree(i) for i in deps])
def _hd(self, i):
try:
return self.nodelist[i]['head']
except IndexError:
return None
def _rel(self, i):
try:
return self.nodelist[i]['rel']
except IndexError:
return None
# what's the return type? Boolean or list?
def contains_cycle(self):
distances = {}
for node in self.nodelist:
for dep in node['deps']:
key = tuple([node['address'], dep]) #'%d -> %d' % (node['address'], dep)
distances[key] = 1
window = 0
for n in range(len(self.nodelist)):
new_entries = {}
for pair1 in distances:
for pair2 in distances:
if pair1[1] == pair2[0]:
key = tuple([pair1[0], pair2[1]])
new_entries[key] = distances[pair1] + distances[pair2]
for pair in new_entries:
distances[pair] = new_entries[pair]
if pair[0] == pair[1]:
print pair[0]
path = self.get_cycle_path(self.get_by_address(pair[0]), pair[0]) #self.nodelist[pair[0]], pair[0])
return path
return False # return []?
def get_cycle_path(self, curr_node, goal_node_index):
for dep in curr_node['deps']:
if dep == goal_node_index:
return [curr_node['address']]
for dep in curr_node['deps']:
path = self.get_cycle_path(self.get_by_address(dep), goal_node_index)#self.nodelist[dep], goal_node_index)
if len(path) > 0:
path.insert(0, curr_node['address'])
return path
return []
def to_conll(self, style):
"""
The dependency graph in CoNLL format.
@param style: the style to use for the format (3, 4, 10 columns)
@type style: C{int}
@rtype: C{str}
"""
lines = []
for i, node in enumerate(self.nodelist[1:]):
word, tag, head, rel = node['word'], node['tag'], node['head'], node['rel']
if style == 3:
lines.append('%s\t%s\t%s\n' % (word, tag, head))
elif style == 4:
lines.append('%s\t%s\t%s\t%s\n' % (word, tag, head, rel))
elif style == 10:
lines.append('%s\t%s\t_\t%s\t%s\t_\t%s\t%s\t_\t_\n' % (i+1, word, tag, tag, head, rel))
else:
raise ValueError('Number of tab-delimited fields (%d) not supported by CoNLL(10) or Malt-Tab(4) format' % (style))
return ''.join(lines)
def nx_graph(self):
"""
Convert the data in a C{nodelist} into a networkx
labeled directed graph.
@rtype: C{XDigraph}
"""
nx_nodelist = range(1, len(self.nodelist))
nx_edgelist = [(n, self._hd(n), self._rel(n))
for n in nx_nodelist if self._hd(n)]
self.nx_labels = {}
for n in nx_nodelist:
self.nx_labels[n] = self.nodelist[n]['word']
g = NX.XDiGraph()
g.add_nodes_from(nx_nodelist)
g.add_edges_from(nx_edgelist)
return g
def demo():
malt_demo()
conll_demo()
conll_file_demo()
cycle_finding_demo()
def malt_demo(nx=False):
"""
A demonstration of the result of reading a dependency
version of the first sentence of the Penn Treebank.
"""
dg = DependencyGraph("""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
""")
tree = dg.tree()
print tree.pprint()
if nx:
#currently doesn't work
try:
import networkx as NX
import pylab as P
except ImportError:
raise
g = dg.nx_graph()
g.info()
pos = NX.spring_layout(g, dim=1)
NX.draw_networkx_nodes(g, pos, node_size=50)
#NX.draw_networkx_edges(g, pos, edge_color='k', width=8)
NX.draw_networkx_labels(g, pos, dg.nx_labels)
P.xticks([])
P.yticks([])
P.savefig('tree.png')
P.show()
def conll_demo():
"""
A demonstration of how to read a string representation of
a CoNLL format dependency tree.
"""
dg = DependencyGraph(conll_data1)
tree = dg.tree()
print tree.pprint()
print dg
print dg.to_conll(4)
def conll_file_demo():
print 'Mass conll_read demo...'
graphs = [DependencyGraph(entry)
for entry in conll_data2.split('\n\n') if entry]
for graph in graphs:
tree = graph.tree()
print '\n' + tree.pprint()
def cycle_finding_demo():
dg = DependencyGraph(treebank_data)
print dg.contains_cycle()
cyclic_dg = DependencyGraph()
top = {'word':None, 'deps':[1], 'rel': 'TOP', 'address': 0}
child1 = {'word':None, 'deps':[2], 'rel': 'NTOP', 'address': 1}
child2 = {'word':None, 'deps':[4], 'rel': 'NTOP', 'address': 2}
child3 = {'word':None, 'deps':[1], 'rel': 'NTOP', 'address': 3}
child4 = {'word':None, 'deps':[3], 'rel': 'NTOP', 'address': 4}
cyclic_dg.nodelist = [top, child1, child2, child3, child4]
cyclic_dg.root = top
print cyclic_dg.contains_cycle()
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
"""
conll_data1 = """
1 Ze ze Pron Pron per|3|evofmv|nom 2 su _ _
2 had heb V V trans|ovt|1of2of3|ev 0 ROOT _ _
3 met met Prep Prep voor 8 mod _ _
4 haar haar Pron Pron bez|3|ev|neut|attr 5 det _ _
5 moeder moeder N N soort|ev|neut 3 obj1 _ _
6 kunnen kan V V hulp|ott|1of2of3|mv 2 vc _ _
7 gaan ga V V hulp|inf 6 vc _ _
8 winkelen winkel V V intrans|inf 11 cnj _ _
9 , , Punc Punc komma 8 punct _ _
10 zwemmen zwem V V intrans|inf 11 cnj _ _
11 of of Conj Conj neven 7 vc _ _
12 terrassen terras N N soort|mv|neut 11 cnj _ _
13 . . Punc Punc punt 12 punct _ _
"""
conll_data2 = """1 Cathy Cathy N N eigen|ev|neut 2 su _ _
2 zag zie V V trans|ovt|1of2of3|ev 0 ROOT _ _
3 hen hen Pron Pron per|3|mv|datofacc 2 obj1 _ _
4 wild wild Adj Adj attr|stell|onverv 5 mod _ _
5 zwaaien zwaai N N soort|mv|neut 2 vc _ _
6 . . Punc Punc punt 5 punct _ _
1 Ze ze Pron Pron per|3|evofmv|nom 2 su _ _
2 had heb V V trans|ovt|1of2of3|ev 0 ROOT _ _
3 met met Prep Prep voor 8 mod _ _
4 haar haar Pron Pron bez|3|ev|neut|attr 5 det _ _
5 moeder moeder N N soort|ev|neut 3 obj1 _ _
6 kunnen kan V V hulp|ott|1of2of3|mv 2 vc _ _
7 gaan ga V V hulp|inf 6 vc _ _
8 winkelen winkel V V intrans|inf 11 cnj _ _
9 , , Punc Punc komma 8 punct _ _
10 zwemmen zwem V V intrans|inf 11 cnj _ _
11 of of Conj Conj neven 7 vc _ _
12 terrassen terras N N soort|mv|neut 11 cnj _ _
13 . . Punc Punc punt 12 punct _ _
1 Dat dat Pron Pron aanw|neut|attr 2 det _ _
2 werkwoord werkwoord N N soort|ev|neut 6 obj1 _ _
3 had heb V V hulp|ovt|1of2of3|ev 0 ROOT _ _
4 ze ze Pron Pron per|3|evofmv|nom 6 su _ _
5 zelf zelf Pron Pron aanw|neut|attr|wzelf 3 predm _ _
6 uitgevonden vind V V trans|verldw|onverv 3 vc _ _
7 . . Punc Punc punt 6 punct _ _
1 Het het Pron Pron onbep|neut|zelfst 2 su _ _
2 hoorde hoor V V trans|ovt|1of2of3|ev 0 ROOT _ _
3 bij bij Prep Prep voor 2 ld _ _
4 de de Art Art bep|zijdofmv|neut 6 det _ _
5 warme warm Adj Adj attr|stell|vervneut 6 mod _ _
6 zomerdag zomerdag N N soort|ev|neut 3 obj1 _ _
7 die die Pron Pron betr|neut|zelfst 6 mod _ _
8 ze ze Pron Pron per|3|evofmv|nom 12 su _ _
9 ginds ginds Adv Adv gew|aanw 12 mod _ _
10 achter achter Adv Adv gew|geenfunc|stell|onverv 12 svp _ _
11 had heb V V hulp|ovt|1of2of3|ev 7 body _ _
12 gelaten laat V V trans|verldw|onverv 11 vc _ _
13 . . Punc Punc punt 12 punct _ _
1 Ze ze Pron Pron per|3|evofmv|nom 2 su _ _
2 hadden heb V V trans|ovt|1of2of3|mv 0 ROOT _ _
3 languit languit Adv Adv gew|geenfunc|stell|onverv 11 mod _ _
4 naast naast Prep Prep voor 11 mod _ _
5 elkaar elkaar Pron Pron rec|neut 4 obj1 _ _
6 op op Prep Prep voor 11 ld _ _
7 de de Art Art bep|zijdofmv|neut 8 det _ _
8 strandstoelen strandstoel N N soort|mv|neut 6 obj1 _ _
9 kunnen kan V V hulp|inf 2 vc _ _
10 gaan ga V V hulp|inf 9 vc _ _
11 liggen lig V V intrans|inf 10 vc _ _
12 . . Punc Punc punt 11 punct _ _
1 Zij zij Pron Pron per|3|evofmv|nom 2 su _ _
2 zou zal V V hulp|ovt|1of2of3|ev 7 cnj _ _
3 mams mams N N soort|ev|neut 4 det _ _
4 rug rug N N soort|ev|neut 5 obj1 _ _
5 ingewreven wrijf V V trans|verldw|onverv 6 vc _ _
6 hebben heb V V hulp|inf 2 vc _ _
7 en en Conj Conj neven 0 ROOT _ _
8 mam mam V V trans|ovt|1of2of3|ev 7 cnj _ _
9 de de Art Art bep|zijdofmv|neut 10 det _ _
10 hare hare Pron Pron bez|3|ev|neut|attr 8 obj1 _ _
11 . . Punc Punc punt 10 punct _ _
1 Of of Conj Conj onder|metfin 0 ROOT _ _
2 ze ze Pron Pron per|3|evofmv|nom 3 su _ _
3 had heb V V hulp|ovt|1of2of3|ev 0 ROOT _ _
4 gewoon gewoon Adj Adj adv|stell|onverv 10 mod _ _
5 met met Prep Prep voor 10 mod _ _
6 haar haar Pron Pron bez|3|ev|neut|attr 7 det _ _
7 vriendinnen vriendin N N soort|mv|neut 5 obj1 _ _
8 rond rond Adv Adv deelv 10 svp _ _
9 kunnen kan V V hulp|inf 3 vc _ _
10 slenteren slenter V V intrans|inf 9 vc _ _
11 in in Prep Prep voor 10 mod _ _
12 de de Art Art bep|zijdofmv|neut 13 det _ _
13 buurt buurt N N soort|ev|neut 11 obj1 _ _
14 van van Prep Prep voor 13 mod _ _
15 Trafalgar_Square Trafalgar_Square MWU N_N eigen|ev|neut_eigen|ev|neut 14 obj1 _ _
16 . . Punc Punc punt 15 punct _ _
"""
if __name__ == '__main__':
demo()
|
