def _init_glue(self):
tagger = RegexpTagger([
('^(David|Mary|John)$', 'NNP'),
('^(walks|sees|eats|chases|believes|gives|sleeps|chases|persuades|tries|seems|leaves)$',
'VB'), ('^(go|order|vanish|find|approach)$', 'VB'),
('^(a)$', 'ex_quant'), ('^(every)$', 'univ_quant'),
('^(sandwich|man|dog|pizza|unicorn|cat|senator)$', 'NN'),
('^(big|gray|former)$', 'JJ'), ('^(him|himself)$', 'PRP')
])
depparser = MaltParser(tagger=tagger)
self._glue = DrtGlue(depparser=depparser, remove_duplicates=False)
def __init__(self, semtype_file=None, remove_duplicates=False,
depparser=None):
"""
:param semtype_file: name of file where grammar can be loaded
:param remove_duplicates: should duplicates be removed?
:param depparser: the dependency parser
"""
if semtype_file is None:
semtype_file = 'drt_glue.semtype'
self._glue = DrtGlue(semtype_file=semtype_file,
remove_duplicates=remove_duplicates,
depparser=depparser)
def tag_sentences(self, text, hyp, verbose=False):
"""
Tag a RTEPair as to whether the hypothesis can be inferred from the text.
"""
glueclass = DrtGlue()
text_drs_list = glueclass.parse_to_meaning(text)
if text_drs_list:
text_ex = text_drs_list[0].simplify().toFol()
else:
if verbose: print 'ERROR: No readings were generated for the Text'
hyp_drs_list = glueclass.parse_to_meaning(hyp)
if hyp_drs_list:
hyp_ex = hyp_drs_list[0].simplify().toFol()
else:
if verbose:
print 'ERROR: No readings were generated for the Hypothesis'
#1. proof T -> H
#2. proof (BK & T) -> H
#3. proof :(BK & T)
#4. proof :(BK & T & H)
#5. satisfy BK & T
#6. satisfy BK & T & H
result = inference.Prover9().prove(hyp_ex, [text_ex])
if verbose: print 'prove: T -> H: %s' % result
if not result:
bk = self._generate_BK(text, hyp, verbose)
bk_exs = [bk_pair[0] for bk_pair in bk]
if verbose:
print 'Generated Background Knowledge:'
for bk_ex in bk_exs:
print bk_ex
result = inference.Prover9().prove(hyp_ex, [text_ex] + bk_exs)
if verbose: print 'prove: (T & BK) -> H: %s' % result
if not result:
consistent = self.check_consistency(bk_exs + [text_ex])
if verbose:
print 'consistency check: (BK & T): %s' % consistent
if consistent:
consistent = self.check_consistency(bk_exs +
[text_ex, hyp_ex])
if verbose:
print 'consistency check: (BK & T & H): %s' % consistent
return result
def demo_inference_tagger(verbose=False):
tagger = RTEInferenceTagger()
text = 'John see a car'
print 'Text: ', text
hyp = 'John watch an auto'
print 'Hyp: ', hyp
# text_ex = LogicParser().parse('exists e x y.(david(x) & own(e) & subj(e,x) & obj(e,y) & car(y))')
# hyp_ex = LogicParser().parse('exists e x y.(david(x) & have(e) & subj(e,x) & obj(e,y) & auto(y))')
glueclass = DrtGlue(verbose=verbose)
text_drs_list = glueclass.parse_to_meaning(text)
if text_drs_list:
text_ex = text_drs_list[0].simplify().toFol()
else:
print 'ERROR: No readings were be generated for the Text'
hyp_drs_list = glueclass.parse_to_meaning(hyp)
if hyp_drs_list:
hyp_ex = hyp_drs_list[0].simplify().toFol()
else:
print 'ERROR: No readings were be generated for the Hypothesis'
print 'Text: ', text_ex
print 'Hyp: ', hyp_ex
print ''
#1. proof T -> H
#2. proof (BK & T) -> H
#3. proof :(BK & T)
#4. proof :(BK & T & H)
#5. satisfy BK & T
#6. satisfy BK & T & H
result = inference.Prover9().prove(hyp_ex, [text_ex])
print 'prove: T -> H: %s' % result
if result:
print 'Logical entailment\n'
else:
print 'No logical entailment\n'
bk = tagger._generate_BK(text, hyp, verbose)
bk_exs = [bk_pair[0] for bk_pair in bk]
print 'Generated Background Knowledge:'
for bk_ex in bk_exs:
print bk_ex
print ''
result = inference.Prover9().prove(hyp_ex, [text_ex] + bk_exs)
print 'prove: (T & BK) -> H: %s' % result
if result:
print 'Logical entailment\n'
else:
print 'No logical entailment\n'
# Check if the background knowledge axioms are inconsistent
result = inference.Prover9().prove(assumptions=bk_exs + [text_ex]).prove()
print 'prove: (BK & T): %s' % result
if result:
print 'Inconsistency -> Entailment unknown\n'
else:
print 'No inconsistency\n'
result = inference.Prover9().prove(assumptions=bk_exs + [text_ex, hyp_ex])
print 'prove: (BK & T & H): %s' % result
if result:
print 'Inconsistency -> Entailment unknown\n'
else:
print 'No inconsistency\n'
result = inference.Mace().build_model(assumptions=bk_exs + [text_ex])
print 'satisfy: (BK & T): %s' % result
if result:
print 'No inconsistency\n'
else:
print 'Inconsistency -> Entailment unknown\n'
result = inference.Mace().build_model(assumptions=bk_exs +
[text_ex, hyp_ex]).build_model()
print 'satisfy: (BK & T & H): %s' % result
if result:
print 'No inconsistency\n'
else:
print 'Inconsistency -> Entailment unknown\n'
