def __init__(self, answer, i):
Feature.set_type(self, clas + rel)
Feature.set_name(self, 'Antonyms')
q = answer.q
sentence = answer.sources[i].sentence
q_verb = q.root_verb[0].lemma_
doc = nlp(sentence)
s1 = []
for s in doc.sents:
s1.append(s)
s_verb = s1[0].root.lemma_
sim = antonym(s_verb, q_verb)
Feature.set_value(self, sim)
def __init__(self, answer, i):
Feature.set_type(self, clas + rel)
Feature.set_name(self, 'Antonyms')
q = answer.q
sentence = answer.sources[i].sentence
q_verb = q.root_verb[0].lemma_
doc = nlp(sentence)
s1 = []
for s in doc.sents:
s1.append(s)
s_verb = s1[0].root.lemma_
sim = antonym(s_verb, q_verb)
Feature.set_value(self, sim)
def __init__(self, answer, i):
Feature.set_type(self, clas + rel)
Feature.set_name(self, 'Verb similarity (WordNet)')
q = answer.q
sentence = answer.sources[i].sentence.split(':')[-1]
q_verb = q.root_verb[0].lemma_
doc = nlp(sentence)
s1 = list(doc.sents)
s_verb = s1[0].root.lemma_
info = 'Qverb=%s, Sverb=%s' % (q_verb, s_verb)
Feature.set_info(self, info)
sim = max_sim(s_verb, q_verb)
Feature.set_value(self, sim)
def __init__(self, answer, i):
Feature.set_type(self, clas + rel)
Feature.set_name(self, 'Verb similarity (WordNet)')
q = answer.q
sentence = answer.sources[i].sentence.split(':')[-1]
q_verb = q.root_verb[0].lemma_
doc = nlp(sentence)
s1 = list(doc.sents)
s_verb = s1[0].root.lemma_
info = 'Qverb=%s, Sverb=%s' % (q_verb, s_verb)
Feature.set_info(self, info)
sim = max_sim(s_verb, q_verb)
Feature.set_value(self, sim)
def __init__(self, answer, i):
Feature.set_type(self, clas + rel)
Feature.set_name(self, 'Verb similarity (spacy)')
q = answer.q
sentence = answer.sources[i].sentence.split(':')[-1]
doc = nlp(sentence)
s1 = list(doc.sents)
for j in range(len(s1)):
if not s1[j].text.isspace():
s_verbs = verbs(s1[j])
break
q_root = q.root_verb[0]
doc = nlp(q_root.lemma_)
s1 = list(doc.sents)
q_root = s1[0][0]
if len(s_verbs
) == 0 or s_verbs[0].lemma_ == 'be' or q_root.lemma_ == 'be':
Feature.set_value(self, 0.)
return
else:
doc = nlp(s_verbs[0].lemma_)
s1 = list(doc.sents)
s_root = s1[0][0]
q_vec = bow([q_root])
s_vec = bow([s_root])
info = ''
for verb in [q_root]:
info += 'Qverb=%s ' % (verb)
for verb in [s_root]:
info += 'Sverb=%s ' % (verb)
Feature.set_info(self, info)
sim = np.dot(q_vec,
s_vec) / (np.linalg.norm(q_vec) * np.linalg.norm(s_vec))
if math.isnan(sim):
sim = 0
Feature.set_value(self, sim)
def __init__(self, answer, i):
Feature.set_type(self, clas + rel)
Feature.set_name(self, 'Verb similarity (spacy)')
q = answer.q
sentence = answer.sources[i].sentence.split(':')[-1]
doc = nlp(sentence)
s1 = list(doc.sents)
for j in range(len(s1)):
if not s1[j].text.isspace():
s_verbs = verbs(s1[j])
break
q_root = q.root_verb[0]
doc = nlp(q_root.lemma_)
s1 = list(doc.sents)
q_root = s1[0][0]
if len(s_verbs) == 0 or s_verbs[0].lemma_ == 'be' or q_root.lemma_ == 'be':
Feature.set_value(self, 0.)
return
else:
doc = nlp(s_verbs[0].lemma_)
s1 = list(doc.sents)
s_root = s1[0][0]
q_vec = bow([q_root])
s_vec = bow([s_root])
info = ''
for verb in [q_root]:
info += 'Qverb=%s ' % (verb)
for verb in [s_root]:
info += 'Sverb=%s ' % (verb)
Feature.set_info(self, info)
sim = np.dot(q_vec, s_vec) / (np.linalg.norm(q_vec) * np.linalg.norm(s_vec))
if math.isnan(sim):
sim = 0
Feature.set_value(self, sim)
def __init__(self, answer, i):
Feature.set_type(self, clas + rel)
Feature.set_name(self, 'Subject match')
sentence = answer.sources[i].sentence.split(':')[-1]
q = answer.q.root_verb[0]
qsubj = get_subj(q)
ssubj = get_subj(list(nlp(sentence).sents)[0].root)
if qsubj is None or ssubj is None:
Feature.set_value(self, 0.)
return
info = 'Qsubject=%s, Ssubject=%s' % (qsubj.text, ssubj.text)
Feature.set_info(self, info)
if qsubj.lower_ in ssubj.lower_ or ssubj.lower_ in qsubj.lower_:
Feature.set_value(self, 1.)
else:
Feature.set_value(self, 0.)
def __init__(self, answer, i):
Feature.set_type(self, clas + rel)
Feature.set_name(self, 'Subject match')
sentence = answer.sources[i].sentence.split(':')[-1]
q = answer.q.root_verb[0]
qsubj = get_subj(q)
ssubj = get_subj(list(nlp(sentence).sents)[0].root)
if qsubj is None or ssubj is None:
Feature.set_value(self, 0.)
return
info = 'Qsubject=%s, Ssubject=%s' % (qsubj.text, ssubj.text)
Feature.set_info(self, info)
if qsubj.lower_ in ssubj.lower_ or ssubj.lower_ in qsubj.lower_:
Feature.set_value(self, 1.)
else:
Feature.set_value(self, 0.)
