Spaces:
Sleeping
Sleeping
File size: 32,468 Bytes
d916065 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 |
# Natural Language Toolkit: Dependency Grammars
#
# Copyright (C) 2001-2023 NLTK Project
# Author: Jason Narad <[email protected]>
# Steven Bird <[email protected]> (modifications)
#
# URL: <https://www.nltk.org/>
# For license information, see LICENSE.TXT
#
"""
Tools for reading and writing dependency trees.
The input is assumed to be in Malt-TAB format
(https://stp.lingfil.uu.se/~nivre/research/MaltXML.html).
"""
import subprocess
import warnings
from collections import defaultdict
from itertools import chain
from pprint import pformat
from nltk.internals import find_binary
from nltk.tree import Tree
#################################################################
# DependencyGraph Class
#################################################################
class DependencyGraph:
"""
A container for the nodes and labelled edges of a dependency structure.
"""
def __init__(
self,
tree_str=None,
cell_extractor=None,
zero_based=False,
cell_separator=None,
top_relation_label="ROOT",
):
"""Dependency graph.
We place a dummy `TOP` node with the index 0, since the root node is
often assigned 0 as its head. This also means that the indexing of the
nodes corresponds directly to the Malt-TAB format, which starts at 1.
If zero-based is True, then Malt-TAB-like input with node numbers
starting at 0 and the root node assigned -1 (as produced by, e.g.,
zpar).
:param str cell_separator: the cell separator. If not provided, cells
are split by whitespace.
:param str top_relation_label: the label by which the top relation is
identified, for examlple, `ROOT`, `null` or `TOP`.
"""
self.nodes = defaultdict(
lambda: {
"address": None,
"word": None,
"lemma": None,
"ctag": None,
"tag": None,
"feats": None,
"head": None,
"deps": defaultdict(list),
"rel": None,
}
)
self.nodes[0].update({"ctag": "TOP", "tag": "TOP", "address": 0})
self.root = None
if tree_str:
self._parse(
tree_str,
cell_extractor=cell_extractor,
zero_based=zero_based,
cell_separator=cell_separator,
top_relation_label=top_relation_label,
)
def remove_by_address(self, address):
"""
Removes the node with the given address. References
to this node in others will still exist.
"""
del self.nodes[address]
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.nodes.values():
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.
"""
relation = self.nodes[mod_address]["rel"]
self.nodes[head_address]["deps"].setdefault(relation, [])
self.nodes[head_address]["deps"][relation].append(mod_address)
# self.nodes[head_address]['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.nodes.values():
for node2 in self.nodes.values():
if node1["address"] != node2["address"] and node2["rel"] != "TOP":
relation = node2["rel"]
node1["deps"].setdefault(relation, [])
node1["deps"][relation].append(node2["address"])
# node1['deps'].append(node2['address'])
def get_by_address(self, node_address):
"""Return the node with the given address."""
return self.nodes[node_address]
def contains_address(self, node_address):
"""
Returns true if the graph contains a node with the given node
address, false otherwise.
"""
return node_address in self.nodes
def to_dot(self):
"""Return a dot representation suitable for using with Graphviz.
>>> dg = DependencyGraph(
... 'John N 2\\n'
... 'loves V 0\\n'
... 'Mary N 2'
... )
>>> print(dg.to_dot())
digraph G{
edge [dir=forward]
node [shape=plaintext]
<BLANKLINE>
0 [label="0 (None)"]
0 -> 2 [label="ROOT"]
1 [label="1 (John)"]
2 [label="2 (loves)"]
2 -> 1 [label=""]
2 -> 3 [label=""]
3 [label="3 (Mary)"]
}
"""
# Start the digraph specification
s = "digraph G{\n"
s += "edge [dir=forward]\n"
s += "node [shape=plaintext]\n"
# Draw the remaining nodes
for node in sorted(self.nodes.values(), key=lambda v: v["address"]):
s += '\n{} [label="{} ({})"]'.format(
node["address"],
node["address"],
node["word"],
)
for rel, deps in node["deps"].items():
for dep in deps:
if rel is not None:
s += '\n{} -> {} [label="{}"]'.format(node["address"], dep, rel)
else:
s += "\n{} -> {} ".format(node["address"], dep)
s += "\n}"
return s
def _repr_svg_(self):
"""Show SVG representation of the transducer (IPython magic).
>>> from nltk.test.setup_fixt import check_binary
>>> check_binary('dot')
>>> dg = DependencyGraph(
... 'John N 2\\n'
... 'loves V 0\\n'
... 'Mary N 2'
... )
>>> dg._repr_svg_().split('\\n')[0]
'<?xml version="1.0" encoding="UTF-8" standalone="no"?>'
"""
dot_string = self.to_dot()
return dot2img(dot_string)
def __str__(self):
return pformat(self.nodes)
def __repr__(self):
return f"<DependencyGraph with {len(self.nodes)} nodes>"
@staticmethod
def load(
filename, zero_based=False, cell_separator=None, top_relation_label="ROOT"
):
"""
:param filename: a name of a file in Malt-TAB format
:param zero_based: nodes in the input file are numbered starting from 0
rather than 1 (as produced by, e.g., zpar)
:param str cell_separator: the cell separator. If not provided, cells
are split by whitespace.
:param str top_relation_label: the label by which the top relation is
identified, for examlple, `ROOT`, `null` or `TOP`.
:return: a list of DependencyGraphs
"""
with open(filename) as infile:
return [
DependencyGraph(
tree_str,
zero_based=zero_based,
cell_separator=cell_separator,
top_relation_label=top_relation_label,
)
for tree_str in infile.read().split("\n\n")
]
def left_children(self, node_index):
"""
Returns the number of left children under the node specified
by the given address.
"""
children = chain.from_iterable(self.nodes[node_index]["deps"].values())
index = self.nodes[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 = chain.from_iterable(self.nodes[node_index]["deps"].values())
index = self.nodes[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.nodes[node["address"]].update(node)
def _parse(
self,
input_,
cell_extractor=None,
zero_based=False,
cell_separator=None,
top_relation_label="ROOT",
):
"""Parse a sentence.
:param extractor: a function that given a tuple of cells returns a
7-tuple, where the values are ``word, lemma, ctag, tag, feats, head,
rel``.
:param str cell_separator: the cell separator. If not provided, cells
are split by whitespace.
:param str top_relation_label: the label by which the top relation is
identified, for examlple, `ROOT`, `null` or `TOP`.
"""
def extract_3_cells(cells, index):
word, tag, head = cells
return index, word, word, tag, tag, "", head, ""
def extract_4_cells(cells, index):
word, tag, head, rel = cells
return index, word, word, tag, tag, "", head, rel
def extract_7_cells(cells, index):
line_index, word, lemma, tag, _, head, rel = cells
try:
index = int(line_index)
except ValueError:
# index can't be parsed as an integer, use default
pass
return index, word, lemma, tag, tag, "", head, rel
def extract_10_cells(cells, index):
line_index, word, lemma, ctag, tag, feats, head, rel, _, _ = cells
try:
index = int(line_index)
except ValueError:
# index can't be parsed as an integer, use default
pass
return index, word, lemma, ctag, tag, feats, head, rel
extractors = {
3: extract_3_cells,
4: extract_4_cells,
7: extract_7_cells,
10: extract_10_cells,
}
if isinstance(input_, str):
input_ = (line for line in input_.split("\n"))
lines = (l.rstrip() for l in input_)
lines = (l for l in lines if l)
cell_number = None
for index, line in enumerate(lines, start=1):
cells = line.split(cell_separator)
if cell_number is None:
cell_number = len(cells)
else:
assert cell_number == len(cells)
if cell_extractor is None:
try:
cell_extractor = extractors[cell_number]
except KeyError as e:
raise ValueError(
"Number of tab-delimited fields ({}) not supported by "
"CoNLL(10) or Malt-Tab(4) format".format(cell_number)
) from e
try:
index, word, lemma, ctag, tag, feats, head, rel = cell_extractor(
cells, index
)
except (TypeError, ValueError):
# cell_extractor doesn't take 2 arguments or doesn't return 8
# values; assume the cell_extractor is an older external
# extractor and doesn't accept or return an index.
word, lemma, ctag, tag, feats, head, rel = cell_extractor(cells)
if head == "_":
continue
head = int(head)
if zero_based:
head += 1
self.nodes[index].update(
{
"address": index,
"word": word,
"lemma": lemma,
"ctag": ctag,
"tag": tag,
"feats": feats,
"head": head,
"rel": rel,
}
)
# Make sure that the fake root node has labeled dependencies.
if (cell_number == 3) and (head == 0):
rel = top_relation_label
self.nodes[head]["deps"][rel].append(index)
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"]))
yield from self.triples(node=dep)
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()
[1, 2, 4, 3]
"""
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 ({}) 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
def dot2img(dot_string, t="svg"):
"""
Create image representation fom dot_string, using the 'dot' program
from the Graphviz package.
Use the 't' argument to specify the image file format, for ex. 'jpeg', 'eps',
'json', 'png' or 'webp' (Running 'dot -T:' lists all available formats).
Note that the "capture_output" option of subprocess.run() is only available
with text formats (like svg), but not with binary image formats (like png).
"""
try:
find_binary("dot")
try:
if t in ["dot", "dot_json", "json", "svg"]:
proc = subprocess.run(
["dot", "-T%s" % t],
capture_output=True,
input=dot_string,
text=True,
)
else:
proc = subprocess.run(
["dot", "-T%s" % t],
input=bytes(dot_string, encoding="utf8"),
)
return proc.stdout
except:
raise Exception(
"Cannot create image representation by running dot from string: {}"
"".format(dot_string)
)
except OSError as e:
raise Exception("Cannot find the dot binary from Graphviz package") from e
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 _ _
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()
|