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_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 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)