def trigger_description(trig_sentdict, ant_sentdict, trigger, POS_TAGS,
AUX_WORDS):
vector = []
trig_words = trigger.get_words()
subtree = trigger.get_subtree()
context_idx = 0
for w in trigger.get_context()['words']:
if w == trig_sentdict['words'][trigger.get_idx()]: break
context_idx += 1
# Features 1,2
vector.append(truth(len(subtree.leaves()) == len(trig_words)))
vector.append(len(trig_words))
# Feature set 3.
pos_tags_dict = {}
for tag in POS_TAGS:
pos_tags_dict[tag] = 0
for tag in trigger.get_context()['pos'][context_idx:len(trig_words)]:
pos_tags_dict[tag] += 1
vector += [pos_tags_dict[tag] for tag in pos_tags_dict]
# Feature sets 4,5,6. Description of the auxiliary.
vector += VC.lemmacategoryvector(
trig_sentdict['lemmas'][trigger.get_idx()])
vector += VC.lemmavector(trig_sentdict['lemmas'][trigger.get_idx()])
vector += VC.auxwordvector(trig_sentdict['words'][trigger.get_idx()],
AUX_WORDS)
return vector
def trigger_description(trig_sentdict, ant_sentdict, trigger, POS_TAGS, AUX_WORDS):
vector = []
trig_words = trigger.get_words()
subtree = trigger.get_subtree()
context_idx = 0
for w in trigger.get_context()['words']:
if w == trig_sentdict['words'][trigger.get_idx()]: break
context_idx += 1
# Features 1,2
vector.append(truth(len(subtree.leaves()) == len(trig_words)))
vector.append(len(trig_words))
# Feature set 3.
pos_tags_dict = {}
for tag in POS_TAGS: pos_tags_dict[tag] = 0
for tag in trigger.get_context()['pos'][context_idx:len(trig_words)]:
pos_tags_dict[tag] += 1
vector += [pos_tags_dict[tag] for tag in pos_tags_dict]
# Feature sets 4,5,6. Description of the auxiliary.
vector += VC.lemmacategoryvector(trig_sentdict['lemmas'][trigger.get_idx()])
vector += VC.lemmavector(trig_sentdict['lemmas'][trigger.get_idx()])
vector += VC.auxwordvector(trig_sentdict['words'][trigger.get_idx()], AUX_WORDS)
return vector
def make_vector(sentdict,
aux,
features,
aux_categories,
aux_lemmas,
aux_words,
surrounding_words,
pos_tags,
pos_bigrams,
make_old=False):
vec = []
if not make_old:
if 'aux' in features:
vec += lemma_category_vector(aux, aux_categories)
vec += lemma_vector(aux, aux_lemmas)
vec += aux_vector(aux, aux_words)
if 'words' in features:
vec += aux_structure_vector(sentdict, aux, 'words',
surrounding_words)
if 'pos' in features:
vec += aux_structure_vector(sentdict, aux, 'pos', pos_tags)
if 'bigrams' in features:
vec += aux_pos_bigrams_vector(sentdict, aux, pos_bigrams)
vec += linguistic_features_vector(sentdict, aux, features)
else:
vec += old_vc.lemmacategoryvector(aux.lemma)
vec += old_vc.lemmavector(aux.lemma)
vec += old_vc.auxwordvector(aux.word, aux_words)
vec += old_vc.myfeaturesvector(sentdict, aux.wordnum, features)
return vec
def make_vector(sentdict, aux, features, aux_categories, aux_lemmas, aux_words, surrounding_words, pos_tags, pos_bigrams, make_old=False):
vec = []
if not make_old:
if 'aux' in features:
vec += lemma_category_vector(aux, aux_categories)
vec += lemma_vector(aux, aux_lemmas)
vec += aux_vector(aux, aux_words)
if 'words' in features:
vec += aux_structure_vector(sentdict, aux, 'words', surrounding_words)
if 'pos' in features:
vec += aux_structure_vector(sentdict, aux, 'pos', pos_tags)
if 'bigrams' in features:
vec += aux_pos_bigrams_vector(sentdict, aux, pos_bigrams)
vec += linguistic_features_vector(sentdict, aux, features)
else:
vec += old_vc.lemmacategoryvector(aux.lemma)
vec += old_vc.lemmavector(aux.lemma)
vec += old_vc.auxwordvector(aux.word, aux_words)
vec += old_vc.myfeaturesvector(sentdict, aux.wordnum, features)
return vec