def dep_parse(self, sentence):
#Lazy-initialize the depparser
if self.depparser is None:
from nltk.parse import MaltParser
self.depparser = MaltParser(tagger=self.get_pos_tagger())
if not self.depparser._trained:
self.train_depparser()
return [self.depparser.parse(sentence, verbose=self.verbose)]
def dep_parse(self, sentence='every cat leaves'):
#Lazy-initialize the depparser
if self.depparser is None:
from nltk.parse import MaltParser
self.depparser = MaltParser(tagger=self.get_pos_tagger())
if not self.depparser._trained:
self.train_depparser()
tokens = sentence.split()
return [self.depparser.parse(tokens, verbose=self.verbose)]
def dep_parse(self, sentence):
"""
Return a dependency graph for the sentence.
:param sentence: the sentence to be parsed
:type sentence: list(str)
:rtype: DependencyGraph
"""
#Lazy-initialize the depparser
if self.depparser is None:
from nltk.parse import MaltParser
self.depparser = MaltParser(tagger=self.get_pos_tagger())
if not self.depparser._trained:
self.train_depparser()
return self.depparser.parse(sentence, verbose=self.verbose)
def dep_parse(self, sentence):
#Lazy-initialize the depparser
if self.depparser is None:
from nltk.parse import MaltParser
self.depparser = MaltParser(tagger=self.get_pos_tagger())
if not self.depparser._trained:
self.train_depparser()
return [self.depparser.parse(sentence, verbose=self.verbose)]
def dep_parse(self, sentence='every cat leaves'):
#Lazy-initialize the depparser
if self.depparser is None:
from nltk.parse import MaltParser
self.depparser = MaltParser(tagger=self.get_pos_tagger())
if not self.depparser._trained:
self.train_depparser()
tokens = sentence.split()
return [self.depparser.parse(tokens, verbose=self.verbose)]
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 demo(show_example=-1):
from nltk.parse import MaltParser
examples = [
"David sees Mary",
"David eats a sandwich",
"every man chases a dog",
"every man believes a dog sleeps",
"John gives David a sandwich",
"John chases himself",
]
# 'John persuades David to order a pizza',
# 'John tries to go',
# 'John tries to find a unicorn',
# 'John seems to vanish',
# 'a unicorn seems to approach',
# 'every big cat leaves',
# 'every gray cat leaves',
# 'every big gray cat leaves',
# 'a former senator leaves',
print("============== DEMO ==============")
tagger = RegexpTagger(
[
("^(David|Mary|John)$", "NNP"),
(
"^(sees|eats|chases|believes|gives|sleeps|chases|persuades|tries|seems|leaves)$",
"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)
glue = Glue(depparser=depparser, verbose=False)
for (i, sentence) in enumerate(examples):
if i == show_example or show_example == -1:
print(f"[[[Example {i}]]] {sentence}")
for reading in glue.parse_to_meaning(sentence.split()):
print(reading.simplify())
print("")
def demo(show_example=-1):
from nltk.parse import MaltParser
examples = [
'David sees Mary',
'David eats a sandwich',
'every man chases a dog',
'every man believes a dog sleeps',
'John gives David a sandwich',
'John chases himself',
]
# 'John persuades David to order a pizza',
# 'John tries to go',
# 'John tries to find a unicorn',
# 'John seems to vanish',
# 'a unicorn seems to approach',
# 'every big cat leaves',
# 'every gray cat leaves',
# 'every big gray cat leaves',
# 'a former senator leaves',
print('============== DEMO ==============')
tagger = RegexpTagger(
[
('^(David|Mary|John)$', 'NNP'),
(
'^(sees|eats|chases|believes|gives|sleeps|chases|persuades|tries|seems|leaves)$',
'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)
glue = Glue(depparser=depparser, verbose=False)
for (i, sentence) in enumerate(examples):
if i == show_example or show_example == -1:
print('[[[Example %s]]] %s' % (i, sentence))
for reading in glue.parse_to_meaning(sentence.split()):
print(reading.simplify())
print('')
def dep_parse(self, sentence):
"""
Return a dependency graph for the sentence.
:param sentence: the sentence to be parsed
:type sentence: list(str)
:rtype: DependencyGraph
"""
#Lazy-initialize the depparser
if self.depparser is None:
from nltk.parse import MaltParser
self.depparser = MaltParser(tagger=self.get_pos_tagger())
if not self.depparser._trained:
self.train_depparser()
return self.depparser.parse(sentence, verbose=self.verbose)
class Glue:
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 train_depparser(self, depgraphs=None):
if depgraphs:
self.depparser.train(depgraphs)
else:
self.depparser.train_from_file(
nltk.data.find(
os.path.join("grammars", "sample_grammars", "glue_train.conll")
)
)
def parse_to_meaning(self, sentence):
readings = []
for agenda in self.parse_to_compiled(sentence):
readings.extend(self.get_readings(agenda))
return readings
def get_readings(self, agenda):
readings = []
agenda_length = len(agenda)
atomics = dict()
nonatomics = dict()
while agenda: # is not empty
cur = agenda.pop()
glue_simp = cur.glue.simplify()
if isinstance(
glue_simp, linearlogic.ImpExpression
): # if cur.glue is non-atomic
for key in atomics:
try:
if isinstance(cur.glue, linearlogic.ApplicationExpression):
bindings = cur.glue.bindings
else:
bindings = linearlogic.BindingDict()
glue_simp.antecedent.unify(key, bindings)
for atomic in atomics[key]:
if not (
cur.indices & atomic.indices
): # if the sets of indices are disjoint
try:
agenda.append(cur.applyto(atomic))
except linearlogic.LinearLogicApplicationException:
pass
except linearlogic.UnificationException:
pass
try:
nonatomics[glue_simp.antecedent].append(cur)
except KeyError:
nonatomics[glue_simp.antecedent] = [cur]
else: # else cur.glue is atomic
for key in nonatomics:
for nonatomic in nonatomics[key]:
try:
if isinstance(
nonatomic.glue, linearlogic.ApplicationExpression
):
bindings = nonatomic.glue.bindings
else:
bindings = linearlogic.BindingDict()
glue_simp.unify(key, bindings)
if not (
cur.indices & nonatomic.indices
): # if the sets of indices are disjoint
try:
agenda.append(nonatomic.applyto(cur))
except linearlogic.LinearLogicApplicationException:
pass
except linearlogic.UnificationException:
pass
try:
atomics[glue_simp].append(cur)
except KeyError:
atomics[glue_simp] = [cur]
for entry in atomics:
for gf in atomics[entry]:
if len(gf.indices) == agenda_length:
self._add_to_reading_list(gf, readings)
for entry in nonatomics:
for gf in nonatomics[entry]:
if len(gf.indices) == agenda_length:
self._add_to_reading_list(gf, readings)
return readings
def _add_to_reading_list(self, glueformula, reading_list):
add_reading = True
if self.remove_duplicates:
for reading in reading_list:
try:
if reading.equiv(glueformula.meaning, self.prover):
add_reading = False
break
except Exception as e:
# if there is an exception, the syntax of the formula
# may not be understandable by the prover, so don't
# throw out the reading.
print("Error when checking logical equality of statements", e)
if add_reading:
reading_list.append(glueformula.meaning)
def parse_to_compiled(self, sentence):
gfls = [self.depgraph_to_glue(dg) for dg in self.dep_parse(sentence)]
return [self.gfl_to_compiled(gfl) for gfl in gfls]
def dep_parse(self, sentence):
"""
Return a dependency graph for the sentence.
:param sentence: the sentence to be parsed
:type sentence: list(str)
:rtype: DependencyGraph
"""
# Lazy-initialize the depparser
if self.depparser is None:
from nltk.parse import MaltParser
self.depparser = MaltParser(tagger=self.get_pos_tagger())
if not self.depparser._trained:
self.train_depparser()
return self.depparser.parse(sentence, verbose=self.verbose)
def depgraph_to_glue(self, depgraph):
return self.get_glue_dict().to_glueformula_list(depgraph)
def get_glue_dict(self):
return GlueDict(self.semtype_file)
def gfl_to_compiled(self, gfl):
index_counter = Counter()
return_list = []
for gf in gfl:
return_list.extend(gf.compile(index_counter))
if self.verbose:
print("Compiled Glue Premises:")
for cgf in return_list:
print(cgf)
return return_list
def get_pos_tagger(self):
from nltk.corpus import brown
regexp_tagger = RegexpTagger(
[
(r"^-?[0-9]+(\.[0-9]+)?$", "CD"), # cardinal numbers
(r"(The|the|A|a|An|an)$", "AT"), # articles
(r".*able$", "JJ"), # adjectives
(r".*ness$", "NN"), # nouns formed from adjectives
(r".*ly$", "RB"), # adverbs
(r".*s$", "NNS"), # plural nouns
(r".*ing$", "VBG"), # gerunds
(r".*ed$", "VBD"), # past tense verbs
(r".*", "NN"), # nouns (default)
]
)
brown_train = brown.tagged_sents(categories="news")
unigram_tagger = UnigramTagger(brown_train, backoff=regexp_tagger)
bigram_tagger = BigramTagger(brown_train, backoff=unigram_tagger)
trigram_tagger = TrigramTagger(brown_train, backoff=bigram_tagger)
# Override particular words
main_tagger = RegexpTagger(
[(r"(A|a|An|an)$", "ex_quant"), (r"(Every|every|All|all)$", "univ_quant")],
backoff=trigram_tagger,
)
return main_tagger
class Glue(object):
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 train_depparser(self, depgraphs=None):
if depgraphs:
self.depparser.train(depgraphs)
else:
self.depparser.train_from_file(nltk.data.find(
os.path.join('grammars', 'sample_grammars',
'glue_train.conll')))
def parse_to_meaning(self, sentence):
readings = []
for agenda in self.parse_to_compiled(sentence):
readings.extend(self.get_readings(agenda))
return readings
def get_readings(self, agenda):
readings = []
agenda_length = len(agenda)
atomics = dict()
nonatomics = dict()
while agenda: # is not empty
cur = agenda.pop()
glue_simp = cur.glue.simplify()
if isinstance(glue_simp, linearlogic.ImpExpression): # if cur.glue is non-atomic
for key in atomics:
try:
if isinstance(cur.glue, linearlogic.ApplicationExpression):
bindings = cur.glue.bindings
else:
bindings = linearlogic.BindingDict()
glue_simp.antecedent.unify(key, bindings)
for atomic in atomics[key]:
if not (cur.indices & atomic.indices): # if the sets of indices are disjoint
try:
agenda.append(cur.applyto(atomic))
except linearlogic.LinearLogicApplicationException:
pass
except linearlogic.UnificationException:
pass
try:
nonatomics[glue_simp.antecedent].append(cur)
except KeyError:
nonatomics[glue_simp.antecedent] = [cur]
else: # else cur.glue is atomic
for key in nonatomics:
for nonatomic in nonatomics[key]:
try:
if isinstance(nonatomic.glue, linearlogic.ApplicationExpression):
bindings = nonatomic.glue.bindings
else:
bindings = linearlogic.BindingDict()
glue_simp.unify(key, bindings)
if not (cur.indices & nonatomic.indices): # if the sets of indices are disjoint
try:
agenda.append(nonatomic.applyto(cur))
except linearlogic.LinearLogicApplicationException:
pass
except linearlogic.UnificationException:
pass
try:
atomics[glue_simp].append(cur)
except KeyError:
atomics[glue_simp] = [cur]
for entry in atomics:
for gf in atomics[entry]:
if len(gf.indices) == agenda_length:
self._add_to_reading_list(gf, readings)
for entry in nonatomics:
for gf in nonatomics[entry]:
if len(gf.indices) == agenda_length:
self._add_to_reading_list(gf, readings)
return readings
def _add_to_reading_list(self, glueformula, reading_list):
add_reading = True
if self.remove_duplicates:
for reading in reading_list:
try:
if reading.equiv(glueformula.meaning, self.prover):
add_reading = False
break
except Exception as e:
#if there is an exception, the syntax of the formula
#may not be understandable by the prover, so don't
#throw out the reading.
print('Error when checking logical equality of statements', e)
pass
if add_reading:
reading_list.append(glueformula.meaning)
def parse_to_compiled(self, sentence):
gfls = [self.depgraph_to_glue(dg) for dg in self.dep_parse(sentence)]
return [self.gfl_to_compiled(gfl) for gfl in gfls]
def dep_parse(self, sentence):
#Lazy-initialize the depparser
if self.depparser is None:
from nltk.parse import MaltParser
self.depparser = MaltParser(tagger=self.get_pos_tagger())
if not self.depparser._trained:
self.train_depparser()
return [self.depparser.parse(sentence, verbose=self.verbose)]
def depgraph_to_glue(self, depgraph):
return self.get_glue_dict().to_glueformula_list(depgraph)
def get_glue_dict(self):
return GlueDict(self.semtype_file)
def gfl_to_compiled(self, gfl):
index_counter = Counter()
return_list = []
for gf in gfl:
return_list.extend(gf.compile(index_counter))
if self.verbose:
print('Compiled Glue Premises:')
for cgf in return_list:
print(cgf)
return return_list
def get_pos_tagger(self):
regexp_tagger = RegexpTagger(
[(r'^-?[0-9]+(.[0-9]+)?$', 'CD'), # cardinal numbers
(r'(The|the|A|a|An|an)$', 'AT'), # articles
(r'.*able$', 'JJ'), # adjectives
(r'.*ness$', 'NN'), # nouns formed from adjectives
(r'.*ly$', 'RB'), # adverbs
(r'.*s$', 'NNS'), # plural nouns
(r'.*ing$', 'VBG'), # gerunds
(r'.*ed$', 'VBD'), # past tense verbs
(r'.*', 'NN') # nouns (default)
])
brown_train = brown.tagged_sents(categories='news')
unigram_tagger = UnigramTagger(brown_train, backoff=regexp_tagger)
bigram_tagger = BigramTagger(brown_train, backoff=unigram_tagger)
trigram_tagger = TrigramTagger(brown_train, backoff=bigram_tagger)
#Override particular words
main_tagger = RegexpTagger(
[(r'(A|a|An|an)$', 'ex_quant'),
(r'(Every|every|All|all)$', 'univ_quant')
], backoff=trigram_tagger)
return main_tagger