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786 lines
30 KiB
Python
786 lines
30 KiB
Python
5 years ago
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# Natural Language Toolkit: Dependency Grammars
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#
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# Copyright (C) 2001-2019 NLTK Project
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# Author: Jason Narad <jason.narad@gmail.com>
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# Steven Bird <stevenbird1@gmail.com> (modifications)
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#
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# URL: <http://nltk.org/>
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# For license information, see LICENSE.TXT
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#
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"""
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Tools for reading and writing dependency trees.
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The input is assumed to be in Malt-TAB format
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(http://stp.lingfil.uu.se/~nivre/research/MaltXML.html).
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"""
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from __future__ import print_function, unicode_literals
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from collections import defaultdict
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from itertools import chain
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from pprint import pformat
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import subprocess
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import warnings
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from six import string_types
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from nltk.tree import Tree
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from nltk.compat import python_2_unicode_compatible
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#################################################################
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# DependencyGraph Class
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#################################################################
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@python_2_unicode_compatible
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class DependencyGraph(object):
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"""
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A container for the nodes and labelled edges of a dependency structure.
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"""
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def __init__(
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self,
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tree_str=None,
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cell_extractor=None,
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zero_based=False,
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cell_separator=None,
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top_relation_label='ROOT',
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):
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"""Dependency graph.
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We place a dummy `TOP` node with the index 0, since the root node is
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often assigned 0 as its head. This also means that the indexing of the
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nodes corresponds directly to the Malt-TAB format, which starts at 1.
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If zero-based is True, then Malt-TAB-like input with node numbers
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starting at 0 and the root node assigned -1 (as produced by, e.g.,
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zpar).
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:param str cell_separator: the cell separator. If not provided, cells
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are split by whitespace.
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:param str top_relation_label: the label by which the top relation is
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identified, for examlple, `ROOT`, `null` or `TOP`.
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"""
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self.nodes = defaultdict(
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lambda: {
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'address': None,
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'word': None,
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'lemma': None,
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'ctag': None,
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'tag': None,
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'feats': None,
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'head': None,
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'deps': defaultdict(list),
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'rel': None,
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}
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)
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self.nodes[0].update({'ctag': 'TOP', 'tag': 'TOP', 'address': 0})
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self.root = None
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if tree_str:
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self._parse(
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tree_str,
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cell_extractor=cell_extractor,
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zero_based=zero_based,
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cell_separator=cell_separator,
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top_relation_label=top_relation_label,
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)
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def remove_by_address(self, address):
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"""
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Removes the node with the given address. References
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to this node in others will still exist.
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"""
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del self.nodes[address]
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def redirect_arcs(self, originals, redirect):
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"""
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Redirects arcs to any of the nodes in the originals list
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to the redirect node address.
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"""
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for node in self.nodes.values():
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new_deps = []
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for dep in node['deps']:
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if dep in originals:
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new_deps.append(redirect)
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else:
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new_deps.append(dep)
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node['deps'] = new_deps
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def add_arc(self, head_address, mod_address):
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"""
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Adds an arc from the node specified by head_address to the
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node specified by the mod address.
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"""
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relation = self.nodes[mod_address]['rel']
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self.nodes[head_address]['deps'].setdefault(relation, [])
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self.nodes[head_address]['deps'][relation].append(mod_address)
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# self.nodes[head_address]['deps'].append(mod_address)
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def connect_graph(self):
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"""
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Fully connects all non-root nodes. All nodes are set to be dependents
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of the root node.
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"""
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for node1 in self.nodes.values():
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for node2 in self.nodes.values():
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if node1['address'] != node2['address'] and node2['rel'] != 'TOP':
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relation = node2['rel']
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node1['deps'].setdefault(relation, [])
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node1['deps'][relation].append(node2['address'])
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# node1['deps'].append(node2['address'])
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def get_by_address(self, node_address):
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"""Return the node with the given address."""
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return self.nodes[node_address]
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def contains_address(self, node_address):
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"""
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Returns true if the graph contains a node with the given node
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address, false otherwise.
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"""
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return node_address in self.nodes
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def to_dot(self):
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"""Return a dot representation suitable for using with Graphviz.
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>>> dg = DependencyGraph(
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... 'John N 2\\n'
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... 'loves V 0\\n'
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... 'Mary N 2'
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... )
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>>> print(dg.to_dot())
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digraph G{
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edge [dir=forward]
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node [shape=plaintext]
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<BLANKLINE>
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0 [label="0 (None)"]
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0 -> 2 [label="ROOT"]
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1 [label="1 (John)"]
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2 [label="2 (loves)"]
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2 -> 1 [label=""]
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2 -> 3 [label=""]
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3 [label="3 (Mary)"]
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}
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"""
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# Start the digraph specification
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s = 'digraph G{\n'
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s += 'edge [dir=forward]\n'
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s += 'node [shape=plaintext]\n'
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# Draw the remaining nodes
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for node in sorted(self.nodes.values(), key=lambda v: v['address']):
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s += '\n%s [label="%s (%s)"]' % (
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node['address'],
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node['address'],
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node['word'],
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)
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for rel, deps in node['deps'].items():
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for dep in deps:
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if rel is not None:
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s += '\n%s -> %s [label="%s"]' % (node['address'], dep, rel)
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else:
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s += '\n%s -> %s ' % (node['address'], dep)
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s += "\n}"
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return s
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def _repr_svg_(self):
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"""Show SVG representation of the transducer (IPython magic).
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>>> dg = DependencyGraph(
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... 'John N 2\\n'
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... 'loves V 0\\n'
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... 'Mary N 2'
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... )
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>>> dg._repr_svg_().split('\\n')[0]
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'<?xml version="1.0" encoding="UTF-8" standalone="no"?>'
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"""
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dot_string = self.to_dot()
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try:
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process = subprocess.Popen(
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['dot', '-Tsvg'],
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stdin=subprocess.PIPE,
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stdout=subprocess.PIPE,
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stderr=subprocess.PIPE,
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universal_newlines=True,
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)
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except OSError:
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raise Exception('Cannot find the dot binary from Graphviz package')
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out, err = process.communicate(dot_string)
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if err:
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raise Exception(
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'Cannot create svg representation by running dot from string: {}'
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''.format(dot_string)
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)
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return out
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def __str__(self):
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return pformat(self.nodes)
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def __repr__(self):
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return "<DependencyGraph with {0} nodes>".format(len(self.nodes))
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@staticmethod
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def load(
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filename, zero_based=False, cell_separator=None, top_relation_label='ROOT'
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):
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"""
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:param filename: a name of a file in Malt-TAB format
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:param zero_based: nodes in the input file are numbered starting from 0
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rather than 1 (as produced by, e.g., zpar)
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:param str cell_separator: the cell separator. If not provided, cells
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are split by whitespace.
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:param str top_relation_label: the label by which the top relation is
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identified, for examlple, `ROOT`, `null` or `TOP`.
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:return: a list of DependencyGraphs
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"""
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with open(filename) as infile:
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return [
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DependencyGraph(
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tree_str,
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zero_based=zero_based,
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cell_separator=cell_separator,
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top_relation_label=top_relation_label,
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)
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for tree_str in infile.read().split('\n\n')
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]
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def left_children(self, node_index):
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"""
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Returns the number of left children under the node specified
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by the given address.
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"""
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children = chain.from_iterable(self.nodes[node_index]['deps'].values())
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index = self.nodes[node_index]['address']
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return sum(1 for c in children if c < index)
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def right_children(self, node_index):
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"""
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Returns the number of right children under the node specified
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by the given address.
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"""
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children = chain.from_iterable(self.nodes[node_index]['deps'].values())
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index = self.nodes[node_index]['address']
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return sum(1 for c in children if c > index)
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def add_node(self, node):
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if not self.contains_address(node['address']):
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self.nodes[node['address']].update(node)
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def _parse(
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self,
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input_,
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cell_extractor=None,
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zero_based=False,
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cell_separator=None,
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top_relation_label='ROOT',
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):
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"""Parse a sentence.
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:param extractor: a function that given a tuple of cells returns a
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7-tuple, where the values are ``word, lemma, ctag, tag, feats, head,
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rel``.
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:param str cell_separator: the cell separator. If not provided, cells
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are split by whitespace.
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:param str top_relation_label: the label by which the top relation is
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identified, for examlple, `ROOT`, `null` or `TOP`.
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"""
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def extract_3_cells(cells, index):
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word, tag, head = cells
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return index, word, word, tag, tag, '', head, ''
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def extract_4_cells(cells, index):
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word, tag, head, rel = cells
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return index, word, word, tag, tag, '', head, rel
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def extract_7_cells(cells, index):
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line_index, word, lemma, tag, _, head, rel = cells
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try:
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index = int(line_index)
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except ValueError:
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# index can't be parsed as an integer, use default
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pass
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return index, word, lemma, tag, tag, '', head, rel
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def extract_10_cells(cells, index):
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line_index, word, lemma, ctag, tag, feats, head, rel, _, _ = cells
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try:
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index = int(line_index)
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except ValueError:
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# index can't be parsed as an integer, use default
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pass
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return index, word, lemma, ctag, tag, feats, head, rel
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extractors = {
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3: extract_3_cells,
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4: extract_4_cells,
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7: extract_7_cells,
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10: extract_10_cells,
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}
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if isinstance(input_, string_types):
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input_ = (line for line in input_.split('\n'))
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lines = (l.rstrip() for l in input_)
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lines = (l for l in lines if l)
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cell_number = None
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for index, line in enumerate(lines, start=1):
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cells = line.split(cell_separator)
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if cell_number is None:
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cell_number = len(cells)
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else:
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assert cell_number == len(cells)
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if cell_extractor is None:
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try:
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cell_extractor = extractors[cell_number]
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except KeyError:
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raise ValueError(
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'Number of tab-delimited fields ({0}) not supported by '
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'CoNLL(10) or Malt-Tab(4) format'.format(cell_number)
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)
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try:
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index, word, lemma, ctag, tag, feats, head, rel = cell_extractor(
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cells, index
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)
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except (TypeError, ValueError):
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# cell_extractor doesn't take 2 arguments or doesn't return 8
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# values; assume the cell_extractor is an older external
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# extractor and doesn't accept or return an index.
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word, lemma, ctag, tag, feats, head, rel = cell_extractor(cells)
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if head == '_':
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continue
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head = int(head)
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if zero_based:
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head += 1
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self.nodes[index].update(
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{
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'address': index,
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'word': word,
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||
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'lemma': lemma,
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'ctag': ctag,
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'tag': tag,
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'feats': feats,
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'head': head,
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'rel': rel,
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||
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}
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||
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)
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||
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# Make sure that the fake root node has labeled dependencies.
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||
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|
if (cell_number == 3) and (head == 0):
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rel = top_relation_label
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||
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self.nodes[head]['deps'][rel].append(index)
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|
||
|
|
if self.nodes[0]['deps'][top_relation_label]:
|
||
|
|
root_address = self.nodes[0]['deps'][top_relation_label][0]
|
||
|
|
self.root = self.nodes[root_address]
|
||
|
|
self.top_relation_label = top_relation_label
|
||
|
|
else:
|
||
|
|
warnings.warn(
|
||
|
|
"The graph doesn't contain a node " "that depends on the root element."
|
||
|
|
)
|
||
|
|
|
||
|
|
def _word(self, node, filter=True):
|
||
|
|
w = node['word']
|
||
|
|
if filter:
|
||
|
|
if w != ',':
|
||
|
|
return w
|
||
|
|
return w
|
||
|
|
|
||
|
|
def _tree(self, i):
|
||
|
|
""" Turn dependency graphs into NLTK trees.
|
||
|
|
|
||
|
|
:param int i: index of a node
|
||
|
|
:return: either a word (if the indexed node is a leaf) or a ``Tree``.
|
||
|
|
"""
|
||
|
|
node = self.get_by_address(i)
|
||
|
|
word = node['word']
|
||
|
|
deps = sorted(chain.from_iterable(node['deps'].values()))
|
||
|
|
|
||
|
|
if deps:
|
||
|
|
return Tree(word, [self._tree(dep) for dep in deps])
|
||
|
|
else:
|
||
|
|
return word
|
||
|
|
|
||
|
|
def tree(self):
|
||
|
|
"""
|
||
|
|
Starting with the ``root`` node, build a dependency tree using the NLTK
|
||
|
|
``Tree`` constructor. Dependency labels are omitted.
|
||
|
|
"""
|
||
|
|
node = self.root
|
||
|
|
|
||
|
|
word = node['word']
|
||
|
|
deps = sorted(chain.from_iterable(node['deps'].values()))
|
||
|
|
return Tree(word, [self._tree(dep) for dep in deps])
|
||
|
|
|
||
|
|
def triples(self, node=None):
|
||
|
|
"""
|
||
|
|
Extract dependency triples of the form:
|
||
|
|
((head word, head tag), rel, (dep word, dep tag))
|
||
|
|
"""
|
||
|
|
|
||
|
|
if not node:
|
||
|
|
node = self.root
|
||
|
|
|
||
|
|
head = (node['word'], node['ctag'])
|
||
|
|
for i in sorted(chain.from_iterable(node['deps'].values())):
|
||
|
|
dep = self.get_by_address(i)
|
||
|
|
yield (head, dep['rel'], (dep['word'], dep['ctag']))
|
||
|
|
for triple in self.triples(node=dep):
|
||
|
|
yield triple
|
||
|
|
|
||
|
|
def _hd(self, i):
|
||
|
|
try:
|
||
|
|
return self.nodes[i]['head']
|
||
|
|
except IndexError:
|
||
|
|
return None
|
||
|
|
|
||
|
|
def _rel(self, i):
|
||
|
|
try:
|
||
|
|
return self.nodes[i]['rel']
|
||
|
|
except IndexError:
|
||
|
|
return None
|
||
|
|
|
||
|
|
# what's the return type? Boolean or list?
|
||
|
|
def contains_cycle(self):
|
||
|
|
"""Check whether there are cycles.
|
||
|
|
|
||
|
|
>>> dg = DependencyGraph(treebank_data)
|
||
|
|
>>> dg.contains_cycle()
|
||
|
|
False
|
||
|
|
|
||
|
|
>>> 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.nodes = {
|
||
|
|
... 0: top,
|
||
|
|
... 1: child1,
|
||
|
|
... 2: child2,
|
||
|
|
... 3: child3,
|
||
|
|
... 4: child4,
|
||
|
|
... }
|
||
|
|
>>> cyclic_dg.root = top
|
||
|
|
|
||
|
|
>>> cyclic_dg.contains_cycle()
|
||
|
|
[3, 1, 2, 4]
|
||
|
|
|
||
|
|
"""
|
||
|
|
distances = {}
|
||
|
|
|
||
|
|
for node in self.nodes.values():
|
||
|
|
for dep in node['deps']:
|
||
|
|
key = tuple([node['address'], dep])
|
||
|
|
distances[key] = 1
|
||
|
|
|
||
|
|
for _ in self.nodes:
|
||
|
|
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]:
|
||
|
|
path = self.get_cycle_path(self.get_by_address(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)
|
||
|
|
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: int
|
||
|
|
:rtype: str
|
||
|
|
"""
|
||
|
|
|
||
|
|
if style == 3:
|
||
|
|
template = '{word}\t{tag}\t{head}\n'
|
||
|
|
elif style == 4:
|
||
|
|
template = '{word}\t{tag}\t{head}\t{rel}\n'
|
||
|
|
elif style == 10:
|
||
|
|
template = (
|
||
|
|
'{i}\t{word}\t{lemma}\t{ctag}\t{tag}\t{feats}\t{head}\t{rel}\t_\t_\n'
|
||
|
|
)
|
||
|
|
else:
|
||
|
|
raise ValueError(
|
||
|
|
'Number of tab-delimited fields ({0}) not supported by '
|
||
|
|
'CoNLL(10) or Malt-Tab(4) format'.format(style)
|
||
|
|
)
|
||
|
|
|
||
|
|
return ''.join(
|
||
|
|
template.format(i=i, **node)
|
||
|
|
for i, node in sorted(self.nodes.items())
|
||
|
|
if node['tag'] != 'TOP'
|
||
|
|
)
|
||
|
|
|
||
|
|
def nx_graph(self):
|
||
|
|
"""Convert the data in a ``nodelist`` into a networkx labeled directed graph."""
|
||
|
|
import networkx
|
||
|
|
|
||
|
|
nx_nodelist = list(range(1, len(self.nodes)))
|
||
|
|
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.nodes[n]['word']
|
||
|
|
|
||
|
|
g = networkx.MultiDiGraph()
|
||
|
|
g.add_nodes_from(nx_nodelist)
|
||
|
|
g.add_edges_from(nx_edgelist)
|
||
|
|
|
||
|
|
return g
|
||
|
|
|
||
|
|
|
||
|
|
class DependencyGraphError(Exception):
|
||
|
|
"""Dependency graph exception."""
|
||
|
|
|
||
|
|
|
||
|
|
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()
|
||
|
|
tree.pprint()
|
||
|
|
if nx:
|
||
|
|
# currently doesn't work
|
||
|
|
import networkx
|
||
|
|
from matplotlib import pylab
|
||
|
|
|
||
|
|
g = dg.nx_graph()
|
||
|
|
g.info()
|
||
|
|
pos = networkx.spring_layout(g, dim=1)
|
||
|
|
networkx.draw_networkx_nodes(g, pos, node_size=50)
|
||
|
|
# networkx.draw_networkx_edges(g, pos, edge_color='k', width=8)
|
||
|
|
networkx.draw_networkx_labels(g, pos, dg.nx_labels)
|
||
|
|
pylab.xticks([])
|
||
|
|
pylab.yticks([])
|
||
|
|
pylab.savefig('tree.png')
|
||
|
|
pylab.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()
|
||
|
|
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()
|
||
|
|
cyclic_dg.add_node({'word': None, 'deps': [1], 'rel': 'TOP', 'address': 0})
|
||
|
|
cyclic_dg.add_node({'word': None, 'deps': [2], 'rel': 'NTOP', 'address': 1})
|
||
|
|
cyclic_dg.add_node({'word': None, 'deps': [4], 'rel': 'NTOP', 'address': 2})
|
||
|
|
cyclic_dg.add_node({'word': None, 'deps': [1], 'rel': 'NTOP', 'address': 3})
|
||
|
|
cyclic_dg.add_node({'word': None, 'deps': [3], 'rel': 'NTOP', 'address': 4})
|
||
|
|
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 _ _
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10 gaan ga V V hulp|inf 9 vc _ _
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11 liggen lig V V intrans|inf 10 vc _ _
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12 . . Punc Punc punt 11 punct _ _
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1 Zij zij Pron Pron per|3|evofmv|nom 2 su _ _
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2 zou zal V V hulp|ovt|1of2of3|ev 7 cnj _ _
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3 mams mams N N soort|ev|neut 4 det _ _
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4 rug rug N N soort|ev|neut 5 obj1 _ _
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5 ingewreven wrijf V V trans|verldw|onverv 6 vc _ _
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6 hebben heb V V hulp|inf 2 vc _ _
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7 en en Conj Conj neven 0 ROOT _ _
|
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8 mam mam V V trans|ovt|1of2of3|ev 7 cnj _ _
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9 de de Art Art bep|zijdofmv|neut 10 det _ _
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10 hare hare Pron Pron bez|3|ev|neut|attr 8 obj1 _ _
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11 . . Punc Punc punt 10 punct _ _
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1 Of of Conj Conj onder|metfin 0 ROOT _ _
|
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|
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2 ze ze Pron Pron per|3|evofmv|nom 3 su _ _
|
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|
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3 had heb V V hulp|ovt|1of2of3|ev 0 ROOT _ _
|
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|
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4 gewoon gewoon Adj Adj adv|stell|onverv 10 mod _ _
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5 met met Prep Prep voor 10 mod _ _
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6 haar haar Pron Pron bez|3|ev|neut|attr 7 det _ _
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7 vriendinnen vriendin N N soort|mv|neut 5 obj1 _ _
|
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8 rond rond Adv Adv deelv 10 svp _ _
|
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|
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9 kunnen kan V V hulp|inf 3 vc _ _
|
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|
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10 slenteren slenter V V intrans|inf 9 vc _ _
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11 in in Prep Prep voor 10 mod _ _
|
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|
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12 de de Art Art bep|zijdofmv|neut 13 det _ _
|
||
|
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13 buurt buurt N N soort|ev|neut 11 obj1 _ _
|
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|
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14 van van Prep Prep voor 13 mod _ _
|
||
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15 Trafalgar_Square Trafalgar_Square MWU N_N eigen|ev|neut_eigen|ev|neut 14 obj1 _ _
|
||
|
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16 . . Punc Punc punt 15 punct _ _
|
||
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"""
|
||
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|
||
|
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if __name__ == '__main__':
|
||
|
|
demo()
|