def ptb_read_tree(source,
return_empty=False,
allow_empty_labels=False,
allow_empty_words=False,
blank_line_coverage=False):
"""Read a single tree from the given PTB file.
The function reads a character at a time, stopping as soon as a tree can be
constructed, so multiple trees on a sinlge line are manageable.
>>> from io import StringIO
>>> file_text = '''(ROOT (S
... (NP-SBJ (NNP Scotty) )
... (VP (VBD did) (RB not)
... (VP (VB go)
... (ADVP (RB back) )
... (PP (TO to)
... (NP (NN school) ))))
... (. .) ))'''
>>> in_file = StringIO(file_text)
>>> ptb_read_tree(in_file)
(ROOT (S (NP-SBJ (NNP Scotty)) (VP (VBD did) (RB not) (VP (VB go) (ADVP (RB back)) (PP (TO to) (NP (NN school))))) (. .)))"""
cur_text = ''
depth = 0
while True:
char = source.read(1)
if char == '':
return None
break
if char == '\n' and cur_text == ' ' and blank_line_coverage:
return "Empty"
if char in '\n\t':
char = ' '
cur_text += char
if char == '(':
depth += 1
elif char == ')':
depth -= 1
if depth == 0:
if '()' in cur_text:
if return_empty:
return "Empty"
cur_text = ''
continue
if '(' in cur_text:
break
tree = tree_from_text(cur_text, allow_empty_labels, allow_empty_words)
ptb_cleaning(tree)
return tree
def conll_read_tree(source,
return_empty=False,
allow_empty_labels=False,
allow_empty_words=False,
blank_line_coverage=False):
"""Read a single tree from the given CoNLL Shared Task OntoNotes data file.
>>> from io import StringIO
>>> in_file = StringIO(CONLL_EXAMPLE)
>>> tree = conll_read_tree(in_file)
>>> print(tree)
(TOP (S (NP (PRP They)) (VP (MD will) (VP (VB remain) (PP (IN on) (NP (NP (DT a) (NML (JJR lower) (HYPH -) (NN priority)) (NN list)) (SBAR (WHNP (WDT that)) (S (VP (VBZ includes) (NP (CD 17) (JJ other) (NNS countries))))))))) (. .)))"""
cur_text = []
while True:
line = source.readline()
# Check if we are out of input
if line == '':
return None
# strip whitespace and see if this is then end of the parse
line = line.strip()
if line == '':
break
cur_text.append(line)
text = ''
for line in cur_text:
if len(line) == 0 or line[0] == '#':
continue
line = line.split()
# escape parentheses to avoid malformed trees
word = line[3].replace('(', '-LRB-').replace(')', '-LRB-')
try:
pos = line[4].replace('(', '[').replace(')', ']')
except IndexError:
raise ValueError('conll file does not contain a POS tag column.')
tree = line[5]
tree = tree.split('*')
text += '%s(%s %s)%s' % (tree[0], pos, word, tree[1])
return tree_from_text(text)
elif test_text == '':
mprint("End of test input", out, 'err')
break
mprint("Sentence %d:" % sent_no, out, 'all')
gold_text = gold_text.strip()
test_text = test_text.strip()
if len(gold_text) == 0:
mprint("No gold tree", out, 'all')
continue
elif len(test_text) == 0:
mprint("Not parsed", out, 'all')
continue
gold_complete_tree = pstree.tree_from_text(gold_text)
treebanks.ptb_cleaning(gold_complete_tree)
gold_tree = treebanks.apply_collins_rules(gold_complete_tree, False)
if gold_tree is None:
mprint("Empty gold tree", out, 'all')
mprint(gold_complete_tree.__repr__(), out, 'all')
mprint(gold_tree.__repr__(), out, 'all')
continue
if '()' in test_text:
mprint("() test tree", out, 'all')
continue
test_complete_tree = pstree.tree_from_text(test_text)
treebanks.ptb_cleaning(test_complete_tree)
test_tree = treebanks.apply_collins_rules(test_complete_tree, False)
if test_tree is None:
gold_text = gold_text.strip()
test_text = test_text.strip()
gold_relaxed_text = flatten_edited_nodes(gold_text)
test_relaxed_text = flatten_edited_nodes(test_text)
print test_text
print test_relaxed_text
if len(gold_text) == 0:
mprint("No gold tree", out, 'all')
continue
elif len(test_text) == 0:
mprint("Not parsed", out, 'all')
continue
gold_complete_tree = pstree.tree_from_text(gold_text,
allow_empty_labels=True)
gold_complete_tree = treebanks.homogenise_tree(gold_complete_tree)
treebanks.ptb_cleaning(gold_complete_tree)
gold_tree = treebanks.apply_collins_rules(gold_complete_tree, False)
gold_relaxed_tree = pstree.tree_from_text(gold_relaxed_text,
allow_empty_labels=True)
gold_relaxed_tree = treebanks.homogenise_tree(gold_relaxed_tree)
treebanks.ptb_cleaning(gold_relaxed_tree)
gold_relaxed_tree = treebanks.apply_collins_rules(
gold_relaxed_tree, False)
if gold_tree is None:
mprint("Empty gold tree", out, 'all')
mprint(gold_complete_tree.__repr__(), out, 'all')
mprint(gold_tree.__repr__(), out, 'all')
continue
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# vim: set ts=2 sw=2 noet:
import sys
from nlp_util import pstree, head_finder
def headed_tree(tree, head_map, depth=0):
ans = ''
if depth > 0:
ans = '\n' + depth * '\t'
ans += '(' + tree.label + ' ' + str(head_finder.get_head(head_map, tree)[1])
if tree.word is not None:
ans += ' ' + tree.word
for subtree in tree.subtrees:
ans += headed_tree(subtree, head_map, depth + 1)
ans += ')'
return ans
if __name__ == '__main__':
print "Running doctest"
import doctest
doctest.testmod()
tree = pstree.tree_from_text("(ROOT (SINV (S (NP (PRP It)) (VP (AUX 's) (NP (NP (DT a) (NN problem)) (SBAR (WHNP (WDT that)) (S (ADVP (RB clearly)) (VP (AUX has) (S (VP (TO to) (VP (VB be) (VP (VBN resolved))))))))))) (VP (VBD said)) (NP (NP (NNP David) (NNP Cooke)) (NP (NP (JJ executive) (NN director)) (PP (IN of) (NP (DT the) (NNP RTC)))))))")
head_map = head_finder.pennconverter_find_heads(tree)
print headed_tree(tree, head_map)
elif test_text == '':
mprint("End of test input", out, 'err')
break
mprint("Sentence %d:" % sent_no, out, 'all')
gold_text = gold_text.strip()
test_text = test_text.strip()
if len(gold_text) == 0:
mprint("No gold tree", out, 'all')
continue
elif len(test_text) == 0:
mprint("Not parsed", out, 'all')
continue
gold_complete_tree = pstree.tree_from_text(gold_text)
treebanks.ptb_cleaning(gold_complete_tree)
gold_tree = treebanks.apply_collins_rules(gold_complete_tree, False)
if gold_tree is None:
mprint("Empty gold tree", out, 'all')
mprint(gold_complete_tree.__repr__(), out, 'all')
mprint(gold_tree.__repr__(), out, 'all')
continue
if '()' in test_text:
mprint("() test tree", out, 'all')
continue
test_complete_tree = pstree.tree_from_text(test_text)
treebanks.ptb_cleaning(test_complete_tree)
test_tree = treebanks.apply_collins_rules(test_complete_tree, False)
if test_tree is None:
def compute_overall_score(gold_file, test_file):
gold_in = open(gold_file).readlines()
test_in = open(test_file).readlines()
stats = {'out_evalb': [0, 0, 0], 'out_relaxed': [0, 0, 0]}
assert len(gold_in) == len(test_in)
for i in range(len(gold_in)):
print "Sent: " + str(i)
gold_text = gold_in[i]
test_text = test_in[i]
if gold_text == '' and test_text == '':
break
elif gold_text == '':
break
elif test_text == '':
break
gold_text = gold_text.strip()
test_text = test_text.strip()
if len(gold_text) == 0:
continue
elif len(test_text) == 0:
continue
gold_complete_tree = pstree.tree_from_text(gold_text,
allow_empty_labels=True)
gold_complete_tree = treebanks.homogenise_tree(gold_complete_tree)
treebanks.ptb_cleaning(gold_complete_tree)
gold_tree = gold_complete_tree
#gold_tree = treebanks.apply_collins_rules(gold_complete_tree, False)
test_complete_tree = pstree.tree_from_text(test_text,
allow_empty_labels=True)
test_complete_tree = treebanks.homogenise_tree(test_complete_tree)
treebanks.ptb_cleaning(test_complete_tree)
test_tree = test_complete_tree
#test_tree = treebanks.apply_collins_rules(test_complete_tree, False)
gold_words = gold_tree.word_yield()
test_words = test_tree.word_yield()
if len(test_words.split()) != len(gold_words.split()):
print "Sentence lengths do not match in sentence..." + str(i)
print "Gold: " + gold_words.__repr__()
print "Test: " + test_words.__repr__()
match_strict, gold_strict, test_strict, _, _ = relaxed_parse_errors.counts_for_prf(
test_tree, gold_tree)
match_relaxed, gold_relaxed, test_relaxed, _, _ = relaxed_parse_errors.relaxed_counts_for_prf(
test_tree, gold_tree)
stats['out_evalb'][0] += match_strict
stats['out_evalb'][1] += gold_strict
stats['out_evalb'][2] += test_strict
p, r, f = nlp_eval.calc_prf(match_strict, gold_strict, test_strict)
print "Eval--Strict Evalb: %.2f %.2f %.2f" % (p * 100, r * 100,
f * 100)
stats['out_relaxed'][0] += match_relaxed
stats['out_relaxed'][1] += gold_relaxed
stats['out_relaxed'][2] += test_relaxed
p, r, f = nlp_eval.calc_prf(match_relaxed, gold_relaxed, test_relaxed)
print "Eval--Relaxed Edit: %.2f %.2f %.2f" % (p * 100, r * 100,
f * 100)
match = stats['out_evalb'][0]
gold = stats['out_evalb'][1]
test = stats['out_evalb'][2]
p, r, f = nlp_eval.calc_prf(match, gold, test)
print "Overall--Standard EVALB %s: %.2f %.2f %.2f" % ('out', p * 100,
r * 100, f * 100)
match = stats['out_relaxed'][0]
gold = stats['out_relaxed'][1]
test = stats['out_relaxed'][2]
p, r, f = nlp_eval.calc_prf(match, gold, test)
print "Overall--Relaxed EDIT %s: %.2f %.2f %.2f" % ('out', p * 100,
r * 100, f * 100)
