def solve_fol_inference(prem_fol, hypo_fol, str_axioms=[], prover=Prover9(timeout=5), p_number=0):
'''Given FOL formulas of a premise and a hypothesis,
extract relevant knowledge axioms from WordNet, and solve the inference
'''
# if one of the formulas is not derived, return neutral
if not prem_fol or not hypo_fol:
return 'NEUTRAL', 'No closed formulas'
# retrieve knowledge from WordNet based on the predicates found in the formulas
axioms = [ semexp.fromstring(a) for a in str_axioms ]
axioms = axioms + wn_axioms([prem_fol, hypo_fol], p_number)
if axioms:
info("Extracted axioms: {}".format(axioms))
info("Premise Formula: {}".format(prem_fol))
info("Hypothesis Formula: {}".format(hypo_fol))
# run prover two times, for checking entailment and contradiction relations
try:
Entails = prover.prove(hypo_fol, [prem_fol] + axioms)
Contradicts = prover.prove(hypo_fol.negate(), [prem_fol] + axioms)
except Exception as e:
warning(e)
return 'NEUTRAL', e
# if only one of Entails and Contradicts is true, then return that label
# in all other cases return NEUTRAL,
# but give a warning if both Entails and Contradicts are true
if Entails:
if not Contradicts:
return 'ENTAILMENT', 'Definite answer'
warning('Both ENTAILMENT and CONTRADICTION')
return 'NEUTRAL', 'Mixed answers'
if Contradicts:
return 'CONTRADICTION', 'Definite answer'
return 'NEUTRAL', 'Definite answer'
def __init__(self, semtype_file=None, remove_duplicates=False,
depparser=None, verbose=False):
self.verbose = verbose
self.remove_duplicates = remove_duplicates
self.depparser = depparser
from nltk import Prover9
self.prover = Prover9()
if semtype_file:
self.semtype_file = semtype_file
else:
self.semtype_file = os.path.join('grammars', 'sample_grammars','glue.semtype')
def __init__(self,
semtype_file=None,
remove_duplicates=False,
depparser=None,
verbose=False):
self.verbose = verbose
self.remove_duplicates = remove_duplicates
self.depparser = depparser
from nltk import Prover9
self.prover = Prover9()
if semtype_file:
self.semtype_file = semtype_file
else:
self.semtype_file = 'glue.semtype'
# %%
from nltk import Prover9, Prover9Command
from nltk.sem import Expression
read_expr = Expression.fromstring
p = Prover9()
p._binary_location = r"C:\Program Files (x86)\Prover9-Mace4\bin-win32"
# %%
assumptions = [
read_expr(p) for p in [
# Every child loves Santa.
"""
all x.( child(x) -> loves(x,Santa) )
""",
# Everyone who loves Santa loves any reindeer.
"""
all x.(
loves(x,Santa) -> (
all y.(reindeer(y)-> loves(x,y))
)
)
""",
# Rudolph is a reindeer, and Rudolph has a red nose.
"""
reindeer(Rudolph) & rednose(Rudolph)
""",
# Anything which has a red nose is weird or is a clown.
"""
all x.(rednose(x) -> weird(x) | clown(x))
import json
from nltk.sem.logic import *
from nltk.inference import *
from nltk import Prover9
from joblib import Parallel, delayed
from data_util import sentence
prover = Prover9()
prover.config_prover9(r"C:\Program Files (x86)\Prover9-Mace4\bin-win32")
def get_label(premise, hypothesis):
#returns a label that is determined from using Prover9 on the first order logic representations
premise_assumptions = Expression.fromstring(premise.assumptions)
hypothesis_assumptions = Expression.fromstring(hypothesis.assumptions)
premise_logical_form = Expression.fromstring(premise.logical_form)
hypothesis_logical_form = Expression.fromstring(hypothesis.logical_form)
negated_hypothesis_logical_form = Expression.fromstring(
"-" + "(" + hypothesis.logical_form + ")")
if prover.prove(
hypothesis_logical_form,
[premise_logical_form, premise_assumptions, hypothesis_assumptions]):
return "entailment"
if prover.prove(
negated_hypothesis_logical_form,
[premise_logical_form, premise_assumptions, hypothesis_assumptions]):
return "contradiction"
return "neutral"
def build_simple_file(name):