def weighted_vectorize(self, text):
res = []
sentences = tools.seperate_sentences(text)
tr_text = self.tr.textrank(text)
for sen in sentences:
tmp = []
tmp_weight = []
sen_words = tools.seperate(sen)
for w in sen_words:
if self.model.wv.vocab.__contains__(w):
tmp.append(self.model.__getitem__(w))
if w in tr_text:
tmp_weight.append(tr_text[w])
else:
tmp_weight.append(1 / len(sen_words))
else:
tmp.append([0] * self.vec_length)
tmp_weight.append(1 / len(sen_words))
for i in range(len(tmp)):
tmp[i] = tools.vector_multi(tmp[i],
tmp_weight[i] / sum(tmp_weight))
sen_vec = tools.vector_add_multi(tmp)
if len(sen_vec) == 0:
print(sen)
res.append(sen_vec)
return res
def unweighted_vectorize(self, text):
res = []
sentences = tools.seperate_sentences(text)
for line in sentences:
tmp = []
for word in tools.seperate(line):
if self.model.wv.vocab.__contains__(word):
wv = self.model.__getitem__(word)
tmp.append(wv)
else:
tmp.append([0] * self.vec_length)
tmp = tools.vector_add_multi(tmp)
tmp = tools.vector_multi(tmp, 1 / (len(tmp)))
res.append(tmp)
return res