def set_possible_ants(self, trigger, pos_tests):
for sentnum in range(max(0, trigger.sentnum - SENTENCE_SEARCH_DISTANCE), trigger.sentnum + 1):
functions = [f for f in pos_tests if hasattr(f, '__call__')]
for i in range(len(self.sentences[sentnum])):
tag = self.sentences[sentnum].pos[i]
# TODO: ADDED SECOND CLAUSE TO THIS IF TO LOWER NUMBER OF CANDIDATES GENERATED
if True in (f(tag) for f in functions): # and not wc.is_aux_lemma(self.sentences[sentnum].lemmas[i]):
phrase = nt.get_nearest_phrase(nt.maketree(self.sentences[sentnum]['tree'][0]), i, pos_tests)
phrase_length = nt.get_phrase_length(phrase)
# if phrase_length <= 2:
# print phrase
for j in range(i, min(i + phrase_length + 1, len(self.sentences[sentnum]))):
if not ant_after_trigger(sentnum, i, j, trigger):
bad = False
for pos_check in [wc.is_preposition, wc.is_punctuation, wc.is_determiner]:
if pos_check(self.sentences[sentnum].pos[j - 1]):
bad = True
if not bad:
ant = self.idxs_to_ant(sentnum, i, j, trigger)
if len(ant.sub_sentdict) > 0:
trigger.add_possible_ant(ant)
def myfeaturesvector(sentdict, idx, features):
vector = []
tree = NT.maketree(sentdict["tree"][0])
subtrees = NT.getsmallestsubtrees(tree)
subtree_positions = NT.get_smallest_subtree_positions(tree, subtree_list=subtrees)
aux = sentdict["lemmas"][idx]
if "my_features" in features:
vector.append(truth(DV.auxccommandsverb(sentdict, idx, tree, subtree_positions)))
vector.append(truth(DV.auxccommandsverbthatcomesafter(sentdict, idx, tree, subtree_positions)))
vector.append(truth(DV.auxisccommandedbyverb(sentdict, idx, tree, subtree_positions)))
vector.append(truth(DV.auxislocallyccommandedbyverb(sentdict, idx, tree, subtree_positions)))
vector.append(truth(DV.auxlocallyccommandsverb(sentdict, idx, tree, subtree_positions)))
vector.append(truth(DV.isccommandedbycontinuationword(sentdict, idx, tree, subtree_positions)))
vector.append(truth(DV.nexttopunct(sentdict, idx, tree, subtree_positions)))
vector.append(truth(DV.isfollowedbypunct(sentdict, idx, end=["."])))
vector.append(truth(DV.previouswordisasorsoorthan(sentdict["words"], idx)))
vector.append(truth(DV.thesamecheck(sentdict["words"], idx)))
vector.append(truth(DV.toprecedesaux(sentdict, idx)))
vector.append(truth(DV.verbfollowsaux(sentdict, idx)))
# TODO: added this new feature!
vector.append(truth(DV.nextwordistoo(sentdict, idx)))
if "my_rules" in features:
vector.append(truth(aux in DV.MODALS and DV.modalcheck(sentdict, idx, tree, subtree_positions)))
vector.append(truth(aux in DV.BE and DV.becheck(sentdict, idx, tree, subtree_positions)))
vector.append(truth(aux in DV.HAVE and DV.havecheck(sentdict, idx, tree, subtree_positions)))
vector.append(truth(aux in DV.DO and DV.docheck(sentdict, idx, tree, subtree_positions)))
vector.append(truth(aux in DV.TO and DV.tocheck(sentdict, idx, tree, subtree_positions)))
vector.append(truth(aux in DV.SO and DV.socheck(sentdict, idx, tree, subtree_positions)))
# This adds a new layer of features by combining all of the ones I had.
if "square_rules" in features:
size = len(vector)
for i in range(0, size):
for j in range(0, size):
if i != j:
vector.append(truth(untruth(vector[i]) and untruth(vector[j])))
if "combine_aux_type" in features:
bools = [aux in DV.MODALS, aux in DV.BE, aux in DV.HAVE, aux in DV.DO, aux in DV.TO, aux in DV.SO]
vec = [v for v in vector]
for v in vec:
for b in bools:
vector.append(truth(untruth(v) and b))
return vector
def testmyrules(classifier, section_start, section_end):
gs_vector = classifier.getgsdata(section_start, section_end)
aux_start, aux_end = classifier.section_split[
section_start], classifier.section_split[section_end]
my_rules_return_vector = []
count = 0
for sentdict in classifier.each_sentence.sentences:
for i in range(0, len(sentdict['lemmas'])):
word = sentdict['lemmas'][i]
if isauxiliary(sentdict, i):
count += 1
if aux_start < count <= aux_end:
tree = NT.maketree(sentdict['tree'][0])
subtree_positions = NT.get_smallest_subtree_positions(tree)
if word in MODALS:
my_rules_return_vector.append(
truth(
modalcheck(sentdict, i, tree,
subtree_positions))
) #Todo: I modified these b/c they were incorrectly written.
elif word in BE:
my_rules_return_vector.append(
truth(becheck(sentdict, i, tree,
subtree_positions)))
elif word in HAVE:
my_rules_return_vector.append(
truth(
havecheck(sentdict, i, tree,
subtree_positions)))
elif word in DO:
my_rules_return_vector.append(
truth(docheck(sentdict, i, tree,
subtree_positions)))
elif word in TO:
my_rules_return_vector.append(
truth(tocheck(sentdict, i, tree,
subtree_positions)))
elif word in SO:
my_rules_return_vector.append(
truth(socheck(sentdict, i, tree,
subtree_positions)))
classifier.compare(gs_vector,
my_rules_return_vector,
section_start - 1,
verbose=False)
def set_possible_ants(self, trigger, pos_tests):
for sentnum in range(
max(0, trigger.sentnum - SENTENCE_SEARCH_DISTANCE),
trigger.sentnum + 1):
functions = [f for f in pos_tests if hasattr(f, '__call__')]
for i in range(len(self.sentences[sentnum])):
tag = self.sentences[sentnum].pos[i]
# TODO: ADDED SECOND CLAUSE TO THIS IF TO LOWER NUMBER OF CANDIDATES GENERATED
if True in (
f(tag) for f in functions
): # and not wc.is_aux_lemma(self.sentences[sentnum].lemmas[i]):
phrase = nt.get_nearest_phrase(
nt.maketree(self.sentences[sentnum]['tree'][0]), i,
pos_tests)
phrase_length = nt.get_phrase_length(phrase)
# if phrase_length <= 2:
# print phrase
for j in range(
i,
min(i + phrase_length + 1,
len(self.sentences[sentnum]))):
if not ant_after_trigger(sentnum, i, j, trigger):
bad = False
for pos_check in [
wc.is_preposition, wc.is_punctuation,
wc.is_determiner
]:
if pos_check(self.sentences[sentnum].pos[j -
1]):
bad = True
if not bad:
ant = self.idxs_to_ant(sentnum, i, j, trigger)
if len(ant.sub_sentdict) > 0:
trigger.add_possible_ant(ant)
def testmyrules(classifier, section_start, section_end):
gs_vector = classifier.getgsdata(section_start, section_end)
aux_start,aux_end = classifier.section_split[section_start], classifier.section_split[section_end]
my_rules_return_vector = []
count = 0
for sentdict in classifier.each_sentence.sentences:
for i in range(0,len(sentdict['lemmas'])):
word = sentdict['lemmas'][i]
if isauxiliary(sentdict, i):
count += 1
if aux_start < count <= aux_end:
tree = NT.maketree(sentdict['tree'][0])
subtree_positions = NT.get_smallest_subtree_positions(tree)
if word in MODALS: my_rules_return_vector.append(truth(modalcheck(sentdict, i, tree, subtree_positions))) #Todo: I modified these b/c they were incorrectly written.
elif word in BE: my_rules_return_vector.append(truth(becheck(sentdict, i, tree, subtree_positions)))
elif word in HAVE: my_rules_return_vector.append(truth(havecheck(sentdict, i, tree, subtree_positions)))
elif word in DO: my_rules_return_vector.append(truth(docheck(sentdict, i, tree, subtree_positions)))
elif word in TO: my_rules_return_vector.append(truth(tocheck(sentdict, i, tree, subtree_positions)))
elif word in SO: my_rules_return_vector.append(truth(socheck(sentdict, i, tree, subtree_positions)))
classifier.compare(gs_vector, my_rules_return_vector, section_start-1, verbose=False)
def get_nltk_tree(self):
return nt.maketree(self.tree_text[0])
def get_sentence_tree(self, i):
return nt.maketree(self.sentences[i]['tree'][0])
def alignment_comparison(trig_sentdict, ant_sentdict, ant, trigger,
word2vec_dict):
vector = []
ant_context_sentdict = ant.get_context()
trig_context_sentdict = trigger.get_context()
ant_head_idx = get_antecedent_head_index(ant_sentdict, ant)
# Feature 1.
ant_auxs = []
for i in range(0, len(ant_sentdict['words'])):
if DV.isauxiliary(ant_sentdict, i):
ant_auxs.append(ant_sentdict['lemmas'][i])
found = False
for aux in ant_auxs:
if aux in trig_sentdict['lemmas']:
vector.append(truth(True))
found = True
break
if not found:
vector.append(truth(False))
# Feature 2.
if ant.get_sentnum() == trigger.get_sentnum():
vector.append(truth(ant_head_idx > trigger.get_idx()))
vector.append(truth(ant_head_idx == trigger.get_idx()))
vector.append(truth(ant_head_idx < trigger.get_idx()))
else:
vector += [0, 0, 0]
# Features 3,4,5.
for k in ['words', 'lemmas', 'pos']:
total = len(ant_context_sentdict[k]) + len(trig_context_sentdict[k])
common = len(
set(ant_context_sentdict[k]).intersection(
trig_context_sentdict[k]))
vector.append(common)
vector.append((2.0 * float(common)) / float(total))
# Feature 6 - number of words between trigger and antecedent.
vector.append(ant.get_sentnum() - trigger.get_sentnum())
if ant.get_sentnum() == trigger.get_sentnum():
vector.append(ant_head_idx - trigger.get_idx())
else:
crt_sentnum = trigger.get_sentnum()
distance = ant_head_idx
while crt_sentnum < ant.get_sentnum():
distance += len(trig_sentdict['words'])
crt_sentnum += 1
vector.append(distance)
# Feature 7.
# First we get the vecs from the Ant NP and average them.
blank_np = False
ant_np_word2vec = []
ant_np_location = ant.get_context()['np']
if ant_np_location != (-1, -1):
ant_np_word2vec = get_average_np_vec(word2vec_dict, ant_sentdict,
ant_np_location[0],
ant_np_location[1])
else:
blank_np = True
# Next we do the same for the Trigger NP.
trig_np_word2vec = []
trig_np_location = trigger.get_context()['np']
if trig_np_location != (-1, -1):
trig_np_word2vec = get_average_np_vec(word2vec_dict, trig_sentdict,
trig_np_location[0],
trig_np_location[1])
else:
blank_np = True
# Adding the angle of the vector between the trigger NP and antecedent NP.
if not blank_np:
ant_length = vector_length(ant_np_word2vec)
trig_length = vector_length(trig_np_word2vec)
try:
angle = angle_btwn_vectors(ant_np_word2vec,
trig_np_word2vec,
v1_length=ant_length,
v2_length=trig_length)
except ValueError:
angle = 90.0
vector.append(angle)
vector.append(truth(angle == 0.0))
else:
vector.append(90.0)
vector.append(truth(90.0 == 0.0))
if not ant_np_word2vec:
vector += [0 for _ in range(0, WORD2VEC_LENGTH)]
else:
vector += ant_np_word2vec
if not trig_np_word2vec:
vector += [0 for _ in range(0, WORD2VEC_LENGTH)]
else:
vector += trig_np_word2vec
# Now for what comes after the head.
ant_head_idx = get_antecedent_head_index(ant_sentdict, ant)
ant_post_head_w2vec = get_average_np_vec(word2vec_dict, ant_sentdict,
ant_head_idx,
len(ant_sentdict['words']))
# if not ant_post_head_w2vec: vector += [0 for i in range(0,WORD2VEC_LENGTH)]
# else: vector += ant_post_head_w2vec
stop_idx = len(trig_sentdict['words'])
for i in range(trigger.get_idx(), len(trig_sentdict['words'])):
if DV.ispunctuation(trig_sentdict['lemmas'][i]):
stop_idx = i
break
post_trig_w2vec = get_average_np_vec(word2vec_dict, trig_sentdict,
trigger.get_idx(), stop_idx)
# if not post_trig_w2vec: vector += [0 for i in range(0,WORD2VEC_LENGTH)]
# else: vector += post_trig_w2vec
if ant_post_head_w2vec and post_trig_w2vec:
try:
post_angle = angle_btwn_vectors(ant_post_head_w2vec,
post_trig_w2vec)
except ValueError:
post_angle = 90.0
vector.append(post_angle)
vector.append(truth(post_angle == 0.0))
else:
vector.append(90.0)
vector.append(truth(90.0 == 0.0))
# Sentenial complement check.
tree = NT.maketree(ant_sentdict['tree'][0])
if NT.dominates(tree, ant.get_subtree(), trigger.get_subtree()):
vector.append(
truth(
NT.has_phrases_between_trees(
ant.get_subtree(), trigger.get_subtree(),
NIELSON_SENTENIAL_COMPLEMENT_PHRASES)))
else:
vector.append(truth(False))
# Features to account for the number of each phrase type between the antecedent and trigger.
phrases_between = [0 for _ in ALL_PHRASES]
if ant.get_sentnum() == trigger.get_sentnum():
for i in range(0, len(phrases_between)):
if NT.has_phrases_between_trees(ant.get_subtree(),
trigger.get_subtree(),
[ALL_PHRASES[i]]):
phrases_between[i] += 1
vector += phrases_between
vector.append(sum(phrases_between))
return vector
def alignment_comparison(trig_sentdict, ant_sentdict, ant, trigger, word2vec_dict):
vector = []
ant_context_sentdict = ant.get_context()
trig_context_sentdict = trigger.get_context()
ant_head_idx = get_antecedent_head_index(ant_sentdict, ant)
# Feature 1.
ant_auxs = []
for i in range(0,len(ant_sentdict['words'])):
if DV.isauxiliary(ant_sentdict, i):
ant_auxs.append(ant_sentdict['lemmas'][i])
found = False
for aux in ant_auxs:
if aux in trig_sentdict['lemmas']:
vector.append(truth(True))
found = True
break
if not found:
vector.append(truth(False))
# Feature 2.
if ant.get_sentnum() == trigger.get_sentnum():
vector.append(truth(ant_head_idx > trigger.get_idx()))
vector.append(truth(ant_head_idx == trigger.get_idx()))
vector.append(truth(ant_head_idx < trigger.get_idx()))
else: vector += [0,0,0]
# Features 3,4,5.
for k in ['words','lemmas','pos']:
total = len(ant_context_sentdict[k])+len(trig_context_sentdict[k])
common = len(set(ant_context_sentdict[k]).intersection(trig_context_sentdict[k]))
vector.append(common)
vector.append((2.0*float(common))/float(total))
# Feature 6 - number of words between trigger and antecedent.
vector.append(ant.get_sentnum()-trigger.get_sentnum())
if ant.get_sentnum() == trigger.get_sentnum(): vector.append(ant_head_idx - trigger.get_idx())
else:
crt_sentnum = trigger.get_sentnum()
distance = ant_head_idx
while crt_sentnum < ant.get_sentnum():
distance += len(trig_sentdict['words'])
crt_sentnum += 1
vector.append(distance)
# Feature 7.
# First we get the vecs from the Ant NP and average them.
blank_np = False
ant_np_word2vec = []
ant_np_location = ant.get_context()['np']
if ant_np_location != (-1,-1):
ant_np_word2vec = get_average_np_vec(word2vec_dict, ant_sentdict, ant_np_location[0], ant_np_location[1])
else: blank_np = True
# Next we do the same for the Trigger NP.
trig_np_word2vec = []
trig_np_location = trigger.get_context()['np']
if trig_np_location != (-1,-1):
trig_np_word2vec = get_average_np_vec(word2vec_dict, trig_sentdict, trig_np_location[0], trig_np_location[1])
else: blank_np = True
# Adding the angle of the vector between the trigger NP and antecedent NP.
if not blank_np:
ant_length = vector_length(ant_np_word2vec)
trig_length = vector_length(trig_np_word2vec)
try:
angle = angle_btwn_vectors(ant_np_word2vec, trig_np_word2vec, v1_length=ant_length, v2_length=trig_length)
except ValueError:
angle = 90.0
vector.append(angle)
vector.append(truth(angle == 0.0))
else:
vector.append(90.0)
vector.append(truth(90.0 == 0.0))
if not ant_np_word2vec:
vector += [0 for _ in range(0,WORD2VEC_LENGTH)]
else:
vector += ant_np_word2vec
if not trig_np_word2vec:
vector += [0 for _ in range(0,WORD2VEC_LENGTH)]
else:
vector += trig_np_word2vec
# Now for what comes after the head.
ant_head_idx = get_antecedent_head_index(ant_sentdict, ant)
ant_post_head_w2vec = get_average_np_vec(word2vec_dict, ant_sentdict, ant_head_idx, len(ant_sentdict['words']))
# if not ant_post_head_w2vec: vector += [0 for i in range(0,WORD2VEC_LENGTH)]
# else: vector += ant_post_head_w2vec
stop_idx = len(trig_sentdict['words'])
for i in range(trigger.get_idx(), len(trig_sentdict['words'])):
if DV.ispunctuation(trig_sentdict['lemmas'][i]):
stop_idx = i
break
post_trig_w2vec = get_average_np_vec(word2vec_dict, trig_sentdict, trigger.get_idx(), stop_idx)
# if not post_trig_w2vec: vector += [0 for i in range(0,WORD2VEC_LENGTH)]
# else: vector += post_trig_w2vec
if ant_post_head_w2vec and post_trig_w2vec:
try:
post_angle = angle_btwn_vectors(ant_post_head_w2vec, post_trig_w2vec)
except ValueError: post_angle = 90.0
vector.append(post_angle)
vector.append(truth(post_angle == 0.0))
else:
vector.append(90.0)
vector.append(truth(90.0 == 0.0))
# Sentenial complement check.
tree = NT.maketree(ant_sentdict['tree'][0])
if NT.dominates(tree, ant.get_subtree(), trigger.get_subtree()):
vector.append(truth( NT.has_phrases_between_trees(ant.get_subtree(), trigger.get_subtree(), NIELSON_SENTENIAL_COMPLEMENT_PHRASES)))
else:
vector.append(truth(False))
# Features to account for the number of each phrase type between the antecedent and trigger.
phrases_between = [0 for _ in ALL_PHRASES]
if ant.get_sentnum() == trigger.get_sentnum():
for i in range(0,len(phrases_between)):
if NT.has_phrases_between_trees(ant.get_subtree(), trigger.get_subtree(), [ALL_PHRASES[i]]):
phrases_between[i] += 1
vector += phrases_between
vector.append(sum(phrases_between))
return vector
def get_nltk_tree(self):
return nt.maketree(self.tree_text[0])
def get_sentence_tree(self, i):
return nt.maketree(self.sentences[i]['tree'][0])
def myfeaturesvector(sentdict, idx, features):
vector = []
tree = NT.maketree(sentdict['tree'][0])
subtrees = NT.getsmallestsubtrees(tree)
subtree_positions = NT.get_smallest_subtree_positions(
tree, subtree_list=subtrees)
aux = sentdict['lemmas'][idx]
if 'my_features' in features:
vector.append(
truth(DV.auxccommandsverb(sentdict, idx, tree, subtree_positions)))
vector.append(
truth(
DV.auxccommandsverbthatcomesafter(sentdict, idx, tree,
subtree_positions)))
vector.append(
truth(
DV.auxisccommandedbyverb(sentdict, idx, tree,
subtree_positions)))
vector.append(
truth(
DV.auxislocallyccommandedbyverb(sentdict, idx, tree,
subtree_positions)))
vector.append(
truth(
DV.auxlocallyccommandsverb(sentdict, idx, tree,
subtree_positions)))
vector.append(
truth(
DV.isccommandedbycontinuationword(sentdict, idx, tree,
subtree_positions)))
vector.append(
truth(DV.nexttopunct(sentdict, idx, tree, subtree_positions)))
vector.append(truth(DV.isfollowedbypunct(sentdict, idx, end=['.'])))
vector.append(
truth(DV.previouswordisasorsoorthan(sentdict['words'], idx)))
vector.append(truth(DV.thesamecheck(sentdict['words'], idx)))
vector.append(truth(DV.toprecedesaux(sentdict, idx)))
vector.append(truth(DV.verbfollowsaux(sentdict, idx)))
# TODO: added this new feature!
vector.append(truth(DV.nextwordistoo(sentdict, idx)))
if 'my_rules' in features:
vector.append(
truth(aux in DV.MODALS
and DV.modalcheck(sentdict, idx, tree, subtree_positions)))
vector.append(
truth(aux in DV.BE
and DV.becheck(sentdict, idx, tree, subtree_positions)))
vector.append(
truth(aux in DV.HAVE
and DV.havecheck(sentdict, idx, tree, subtree_positions)))
vector.append(
truth(aux in DV.DO
and DV.docheck(sentdict, idx, tree, subtree_positions)))
vector.append(
truth(aux in DV.TO
and DV.tocheck(sentdict, idx, tree, subtree_positions)))
vector.append(
truth(aux in DV.SO
and DV.socheck(sentdict, idx, tree, subtree_positions)))
# This adds a new layer of features by combining all of the ones I had.
if 'square_rules' in features:
size = len(vector)
for i in range(0, size):
for j in range(0, size):
if i != j:
vector.append(
truth(untruth(vector[i]) and untruth(vector[j])))
if 'combine_aux_type' in features:
bools = [
aux in DV.MODALS, aux in DV.BE, aux in DV.HAVE, aux in DV.DO, aux
in DV.TO, aux in DV.SO
]
vec = [v for v in vector]
for v in vec:
for b in bools:
vector.append(truth(untruth(v) and b))
return vector
