def demo():
from nltk.sem import cooper_storage as cs
sentence = "every girl chases a dog"
#sentence = "a man gives a bone to every dog"
print()
print("Analyis of sentence '%s'" % sentence)
print("=" * 50)
trees = cs.parse_with_bindops(sentence, trace=0)
for tree in trees:
semrep = cs.CooperStore(tree.node['SEM'])
print()
print("Binding operators:")
print("-" * 15)
for s in semrep.store:
print(s)
print()
print("Core:")
print("-" * 15)
print(semrep.core)
print()
print("S-Retrieval:")
print("-" * 15)
semrep.s_retrieve(trace=True)
print("Readings:")
print("-" * 15)
for i, reading in enumerate(semrep.readings):
print("%s: %s" % (i + 1, reading))
def parse_cooper_storage(tree):
semantic_representation = cs.CooperStore(tree.label()['SEM'])
print semantic_representation.s_retrieve, 'k'
semantic_representation.s_retrieve()
for reading in semantic_representation.readings:
print 'Reading %s' % reading
print 'Simplified Reading %s' % reading.simplify()
print 'Predicates: ', reading.predicates()
print 'Variables: ', reading.variables()
def sem_parser(sents, syntax, verbose=False, is_cs=False):
"""
It parses sentences with an FDFG grammar and returns a dictionary of
sentences to their semantic representations.
Parameters:
sents: a list of sentences to be parsed.
fcfg_string: a string listing all fcfg rules with SEM for the
FeatureGrammar.
verbose: boolean value. default value is `False`.
if verbose is True it prints results.
is_cs: boolean value. Inicating if it is using Cooper Storage. Default value is `False`.
Returns:
dict: dictionary of sentences translated to a list of their
semantic representaions.
"""
sents_reps = {sent: [] for sent in sents}
for sent, results in zip(sents, nltk.interpret_sents(sents, syntax)):
if verbose:
display(Markdown(f"----\n{sent}: {len(results)} result(s)"))
for j, (synrep, semrep) in enumerate(results):
if is_cs:
cs_semrep = cs.CooperStore(semrep)
cs_semrep.s_retrieve(trace=False)
for reading in cs_semrep.readings:
sents_reps[sent].append(reading)
if verbose:
display_latex(
reading) # prints the SEM feature of a tree
if verbose:
display_tree(synrep) # show the parse tree
else:
sents_reps[sent].append(semrep)
if verbose:
display_latex(semrep) # prints the SEM feature of a tree
display_tree(synrep) # show the parse tree
return sents_reps
print(expr2)
expr1 == expr2
expr3 = read_expr('\P.(exists x.P(x))(\y.see(y, x))')
print(expr3)
print(expr3.simplify())
##量词歧义
from nltk.sem import cooper_storage as cs
sentence = 'every girl chases a dog'
trees = cs.parse_with_bindops(sentence,
grammar='grammars/book_grammars/storage.fcfg')
semrep = trees[0].label()['SEM']
cs_semrep = cs.CooperStore(semrep)
print(cs_semrep.core)
for bo in cs_semrep.store:
print(bo)
cs_semrep.s_retrieve(trace=True)
for reading in cs_semrep.readings:
print(reading)
#段落语义理解
read_dexpr = nltk.sem.DrtExpression.fromstring
drs1 = read_dexpr('([x, y], [angus(x), dog(y), own(x, y)])')
print(drs1)
