def nearest_trig_np(trig, sentences, all_tags, get_words=False):
"""
@type trig: vpe_objects.Auxiliary
@type sentences: vpe_objects.AllSentences
"""
t = sentences.get_sentence_tree(trig.sentnum)
tree_tuples = nt.pos_word_tuples(t)
all_nps = nt.find_subtree_phrases(t, ['NP', 'NP-PRD'])
trig_tup = (trig.pos, trig.word)
trig_tup_idx = tree_tuples.index(trig_tup)
closest_np_value = 99
closest_np = None
for NP in all_nps:
last_np_word_idx = tree_tuples.index(nt.pos_word_tuples(NP)[-1])
if abs(trig_tup_idx - last_np_word_idx) < closest_np_value:
closest_np_value = trig_tup_idx - last_np_word_idx
closest_np = NP
if closest_np == None:
closest_np = t
if get_words:
try:
return closest_np.leaves()
except AttributeError:
return []
np_pos = [
subtree.label() for subtree in nt.getsmallestsubtrees(closest_np)
]
return encode_pos_tag_frequencies(np_pos, all_tags)
def nexttopunct(sentdict, auxidx, t, word_positions_in_tree):
localt = nltktree.generate_local_structure_from_subtree(t, t[word_positions_in_tree[auxidx-1]])
local_word_subtrees = nltktree.getsmallestsubtrees(localt)
try:
checkpuncttag = sentdict['pos'][auxidx+1]
if isperiod(checkpuncttag) or iscomma(checkpuncttag) or isdashorcolon(checkpuncttag):
endbool = True
# for subtree in local_word_subtrees:
# if isverb( subtree.label() ) and subtree != t[word_positions_in_tree[auxidx-1]]:
# if nltktree.ccommands(localt, subtree, t[word_positions_in_tree[auxidx-1]]):
# endbool = False
# break
if endbool: return endbool
except IndexError:
return False
try:
checkpuncttag = sentdict['pos'][auxidx+2]
if sentdict['lemmas'][auxidx+1] == 'not' and (isperiod(checkpuncttag) or iscomma(checkpuncttag) or isdashorcolon(checkpuncttag)):
endbool = True
# for subtree in local_word_subtrees:
# if isverb( subtree.label() ):
# if nltktree.ccommands(localt, subtree, t[word_positions_in_tree[auxidx-1]]):
# endbool = False
# break
if endbool: return endbool
except IndexError:
return False
return False
def nearest_trig_np(trig, sentences, all_tags, get_words=False):
"""
@type trig: vpe_objects.Auxiliary
@type sentences: vpe_objects.AllSentences
"""
t = sentences.get_sentence_tree(trig.sentnum)
tree_tuples = nt.pos_word_tuples(t)
all_nps = nt.find_subtree_phrases(t, ['NP','NP-PRD'])
trig_tup = (trig.pos, trig.word)
trig_tup_idx = tree_tuples.index(trig_tup)
closest_np_value = 99
closest_np = None
for NP in all_nps:
last_np_word_idx = tree_tuples.index(nt.pos_word_tuples(NP)[-1])
if abs(trig_tup_idx - last_np_word_idx) < closest_np_value:
closest_np_value = trig_tup_idx - last_np_word_idx
closest_np = NP
if closest_np == None:
closest_np = t
if get_words:
try:
return closest_np.leaves()
except AttributeError:
return []
np_pos = [subtree.label() for subtree in nt.getsmallestsubtrees(closest_np)]
return encode_pos_tag_frequencies(np_pos, all_tags)
def nearest_ant_np(ant, sentences, all_tags, get_words=False):
"""
@type ant: vpe_objects.Antecedent
@type sentences: vpe_objects.AllSentences
"""
t = sentences.get_sentence_tree(ant.sentnum)
tree_tuples = nt.pos_word_tuples(t)
all_nps = nt.find_subtree_phrases(t, ['NP','NP-PRD'])
ant_tup = (ant.sub_sentdict.pos[len(ant.sub_sentdict)/2], ant.sub_sentdict.words[len(ant.sub_sentdict)/2])
if ant.sentnum != ant.trigger.sentnum:
ant_tup_idx = len(tree_tuples)
else:
ant_tup_idx = tree_tuples.index(ant_tup)
closest_np_value = 99
closest_np = None
for NP in all_nps:
last_np_word_idx = tree_tuples.index(nt.pos_word_tuples(NP)[-1])
if abs(ant_tup_idx - last_np_word_idx) < closest_np_value:
closest_np_value = ant_tup_idx - last_np_word_idx
closest_np = NP
if get_words:
try:
return closest_np.leaves()
except AttributeError:
return []
try:
np_pos = [subtree.label() for subtree in nt.getsmallestsubtrees(closest_np)]
except AttributeError:
np_pos = []
return encode_pos_tag_frequencies(np_pos, all_tags)
def do_rule(sentdict, aux, tree, word_positions_in_tree):
auxidx = aux.wordnum
try:
if sentdict.lemmas[auxidx+1] == 'that':
return True
except IndexError: pass
if not aux_locally_ccommanded_by_verb(sentdict, aux, tree, word_positions_in_tree):
if to_precedes_aux(sentdict, aux): return False
localt = nt.generate_local_structure_from_subtree(tree, tree[word_positions_in_tree[auxidx-1]])
local_word_subtrees = nt.getsmallestsubtrees(localt)
try:
checkpuncttag = sentdict.pos[auxidx+1]
if is_period(checkpuncttag) or is_comma(checkpuncttag) or is_dash_or_colon(checkpuncttag):
endbool = True
for subtree in local_word_subtrees:
if is_verb(subtree.label()) and subtree != tree[word_positions_in_tree[auxidx-1]]:
if nt.ccommands(localt, subtree, tree[word_positions_in_tree[auxidx-1]]):
endbool = False
break
if endbool:
return endbool
except IndexError:
pass
# Don't at the end of sentence.
try:
checkpuncttag = sentdict.pos[auxidx+2]
if sentdict.lemmas[auxidx+1] == 'not' and (is_period(checkpuncttag) or is_comma(checkpuncttag) or is_dash_or_colon(checkpuncttag)):
endbool = True
for subtree in local_word_subtrees:
if is_verb(subtree.label()):
if nt.ccommands(localt, subtree, tree[word_positions_in_tree[auxidx-1]]):
endbool = False
break
if endbool:
return endbool
except IndexError:
pass
if is_ccommanded_by_continuation_word(sentdict ,aux, tree, word_positions_in_tree):
return True
if verb_follows_aux(sentdict, aux):
return False
try:
if is_preposition(sentdict.pos[auxidx+1]) and sentdict.words[auxidx] != 'done':
return True
except IndexError:
pass
return False
def auxccommandsverb(sentdict, auxidx, t, word_positions_in_tree):
subtrees = nltktree.getsmallestsubtrees(t)
for subtree in subtrees:
if isverb(subtree.label()):
try:
if nltktree.ccommands(t, t[word_positions_in_tree[auxidx-1]], subtree):
return True
except IndexError:
pass
return False
def aux_ccommanded_by_verb(sentdict, aux, tree, word_positions_in_tree):
subtrees = nt.getsmallestsubtrees(tree)
for subtree in subtrees:
if is_verb(subtree.label()):
try:
if nt.ccommands(tree, subtree, tree[word_positions_in_tree[aux.wordnum-1]]):
return True
except IndexError:
pass
return False
def auxlocallyccommandsverb(sentdict, auxidx, t, word_positions_in_tree):
try:
localt = nltktree.generate_local_structure_from_subtree(t, t[word_positions_in_tree[auxidx-1]])
local_word_subtrees = nltktree.getsmallestsubtrees(localt)
for subtree in local_word_subtrees:
if isverb(subtree.label()):
if nltktree.ccommands(localt, t[word_positions_in_tree[auxidx-1]], subtree)\
and not nltktree.ccommands(localt, subtree, t[word_positions_in_tree[auxidx-1]]):
return True
except IndexError: pass
return False
def aux_locally_ccommands_verb(sentdict, aux, tree, word_positions_in_tree):
try:
localt = nt.generate_local_structure_from_subtree(tree, tree[word_positions_in_tree[aux.wordnum-1]])
local_word_subtrees = nt.getsmallestsubtrees(localt)
for subtree in local_word_subtrees:
if is_verb(subtree.label()):
if nt.ccommands(localt, tree[word_positions_in_tree[aux.wordnum-1]], subtree)\
and not nt.ccommands(localt, subtree, tree[word_positions_in_tree[aux.wordnum-1]]):
return True
except IndexError: pass
return False
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 nearest_ant_np(ant, sentences, all_tags, get_words=False):
"""
@type ant: vpe_objects.Antecedent
@type sentences: vpe_objects.AllSentences
"""
t = sentences.get_sentence_tree(ant.sentnum)
tree_tuples = nt.pos_word_tuples(t)
all_nps = nt.find_subtree_phrases(t, ['NP', 'NP-PRD'])
ant_tup = (ant.sub_sentdict.pos[len(ant.sub_sentdict) / 2],
ant.sub_sentdict.words[len(ant.sub_sentdict) / 2])
if ant.sentnum != ant.trigger.sentnum:
ant_tup_idx = len(tree_tuples)
else:
ant_tup_idx = tree_tuples.index(ant_tup)
closest_np_value = 99
closest_np = None
for NP in all_nps:
last_np_word_idx = tree_tuples.index(nt.pos_word_tuples(NP)[-1])
if abs(ant_tup_idx - last_np_word_idx) < closest_np_value:
closest_np_value = ant_tup_idx - last_np_word_idx
closest_np = NP
if get_words:
try:
return closest_np.leaves()
except AttributeError:
return []
try:
np_pos = [
subtree.label() for subtree in nt.getsmallestsubtrees(closest_np)
]
except AttributeError:
np_pos = []
return encode_pos_tag_frequencies(np_pos, all_tags)
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 docheck(sentdict, auxidx, t, word_positions_in_tree, verbose=False):
# We DO NOT want to consider 'do so' or 'do the same' sentences here!
"""try:
if sentdict['lemmas'][auxidx+1] == 'so' or (sentdict['lemmas'][auxidx+1] == 'the' and sentdict['lemmas'][auxidx+2] == 'same'):
return False
# We can be POSITIVE that there is NO vpe if we have 'don't do ...' or 'x does do ...'
if sentdict['lemmas'][auxidx-1] == 'do' or (sentdict['lemmas'][auxidx-2] == 'do' and sentdict['lemmas'][auxidx-1] == 'not'):
return False
if sentdict['lemmas'][auxidx+1] == 'do' or (sentdict['lemmas'][auxidx+1] == 'do' and sentdict['lemmas'][auxidx+2] == 'not'):
return False
except IndexError: pass"""
try:
if sentdict['lemmas'][auxidx+1] == 'that':
return True
except IndexError: pass
if not auxislocallyccommandedbyverb(sentdict, auxidx, t, word_positions_in_tree):
# # If 'do' locally c-commands a verb AND is locally c-commanded by a verb, we can be basically 100% sure that there is no VPE.
# if auxislocallyccommandedbyverb(sentdict, auxidx, tree, word_positions_in_tree) and auxlocallyccommandsverb(sentdict, auxidx, tree, word_positions_in_tree):
# return False
if toprecedesaux(sentdict, auxidx): return False
localt = nltktree.generate_local_structure_from_subtree(t, t[word_positions_in_tree[auxidx-1]])
local_word_subtrees = nltktree.getsmallestsubtrees(localt)
# Do at the end of sentence.
checkpuncttag = sentdict['pos'][auxidx+1]
if isperiod(checkpuncttag) or iscomma(checkpuncttag) or isdashorcolon(checkpuncttag):
endbool = True
for subtree in local_word_subtrees:
if isverb( subtree.label() ) and subtree != t[word_positions_in_tree[auxidx-1]]:
if nltktree.ccommands(localt, subtree, t[word_positions_in_tree[auxidx-1]]):
endbool = False
break
if endbool: return endbool
# Don't at the end of sentence.
try:
checkpuncttag = sentdict['pos'][auxidx+2]
if sentdict['lemmas'][auxidx+1] == 'not' and (isperiod(checkpuncttag) or iscomma(checkpuncttag) or isdashorcolon(checkpuncttag)):
endbool = True
for subtree in local_word_subtrees:
if isverb( subtree.label() ):
if nltktree.ccommands(localt, subtree, t[word_positions_in_tree[auxidx-1]]):
endbool = False
break
if endbool: return endbool
except IndexError:
pass
# Small increase in recall, decrease in precision from this.
# numverbs = 0
# for subtree in local_word_subtrees:
# if isverb(subtree.label()) or isnounorprep(subtree.label()):
# numverbs+=1
# if numverbs == 1:
# return True
if isccommandedbycontinuationword(sentdict ,auxidx, t, word_positions_in_tree):
#if not auxlocallyccommandsverb(sentdict ,auxidx, tree, word_positions_in_tree): # 8% recall traded for 4% precision.
#if (not isverb(sentdict['pos'][auxidx+1])) or (sentdict['lemmas'][auxidx+1]=='not' and not isverb(sentdict['pos'][auxidx+2])):
return True
# if not auxccommandsverbthatcomesafter(sentdict ,auxidx, tree, word_positions_in_tree):
# return True
if verbfollowsaux(sentdict, auxidx):
return False
if isprep(sentdict['pos'][auxidx+1]) and sentdict['words'][auxidx] != 'done':
return True
return False
