def choose_the_best_clause(union, question):
if USE_COSINE:
cos = Cosine(1)
article_and_clause_no = []
max_cosine = 0
for ref in union:
article, clause = ref[1]
clause = corpus[article]['clauses'][clause]['text']
cur_cos = cos.similarity(clause, question)
if max_cosine < cur_cos:
max_cosine = cur_cos
article_and_clause_no = ref[1]
return article_and_clause_no
else:
union.sort(key=lambda x: x[0], reverse=True)
return union[0][1]
def similarity(self, question, answer):
stopword = self.read_from(folder_path + '上证专用停用词.txt')
stopwords = []
for sw in stopword:
sw = sw.strip('\n')
sw = sw.strip(' ')
stopwords.append(sw)
# print(stopwords)
meaningful_words1 = []
meaningful_words2 = []
words2 = jieba.cut(str(question))
words3 = jieba.cut(str(answer))
for word in words2:
if word not in stopwords:
meaningful_words1.append(word)
for word in words3:
if word not in stopwords:
meaningful_words2.append(word)
s2 = ''.join(meaningful_words1)
# print(s2)
s3 = ''.join(meaningful_words2)
a1 = Cosine(1)
b1 = Damerau()
c1 = Jaccard(1)
d1 = JaroWinkler()
e1 = Levenshtein()
f1 = LongestCommonSubsequence()
g1 = MetricLCS()
h1 = NGram(2)
i1 = NormalizedLevenshtein()
j1 = OptimalStringAlignment()
k1 = QGram(1)
l1 = SorensenDice(2)
m1 = WeightedLevenshtein(character_substitution=CharSub())
line_sim = []
cos_s = a1.similarity(s2, s3)
line_sim.append(cos_s)
cos_d = a1.distance(s2, s3)
line_sim.append(cos_d)
dam = b1.distance(s2, s3)
line_sim.append(dam)
jac_d = c1.distance(s2, s3)
line_sim.append(jac_d)
jac_s = c1.similarity(s2, s3)
line_sim.append(jac_s)
jar_d = d1.distance(s2, s3)
line_sim.append(jar_d)
jar_s = d1.similarity(s2, s3)
line_sim.append(jar_s)
lev = e1.distance(s2, s3)
line_sim.append(lev)
lon = f1.distance(s2, s3)
line_sim.append(lon)
met = g1.distance(s2, s3)
line_sim.append(met)
ngr = h1.distance(s2, s3)
line_sim.append(ngr)
nor_d = i1.distance(s2, s3)
line_sim.append(nor_d)
nor_s = i1.similarity(s2, s3)
line_sim.append(nor_s)
opt = j1.distance(s2, s3)
line_sim.append(opt)
qgr = k1.distance(s2, s3)
line_sim.append(qgr)
sor_d = l1.distance(s2, s3)
line_sim.append(sor_d)
sor_s = l1.similarity(s2, s3)
line_sim.append(sor_s)
wei = m1.distance(s2, s3)
line_sim.append(wei)
return line_sim
from similarity.levenshtein import Levenshtein
from similarity.normalized_levenshtein import NormalizedLevenshtein
from similarity.cosine import Cosine
lev = Levenshtein()
nolev = NormalizedLevenshtein()
cosine = Cosine(4)
str1 = 'I enjoy playing football'
str2 = 'I love to play soccer'
print(lev.distance(str1, str2))
print('Levenshtein distance:')
print(nolev.similarity(str1, str2))
print('Cosine similarity:')
print(cosine.similarity(str1, str2))