ans_c = prenlp.preprocess(q.c)
ans_d = prenlp.preprocess(q.d)
print "Question: " + str(q.question)
print "A: " + str(q.a)
print "B: " + str(q.b)
print "C: " + str(q.c)
print "D: " + str(q.d)
# Generate Semantic Graph from Question
a = Association(filter="/c/en", limit=30)
semnet = a.get_similar_concepts_by_term_list(ques)
r = Result(semnet)
# Parse Similarity
similar = r.get_similar()
if len(similar) > 0:
# Splice Leading API Directory
for word in similar:
word[0] = word[0][6:]
print "\n"
print similar
print "\n"
# Compute Score Probabilities
prob = [str(computeScore(similar, ans_a)),
str(computeScore(similar, ans_b)),
str(computeScore(similar, ans_c)),
str(computeScore(similar, ans_d))]
ans_c = prenlp.preprocess(q.c)
ans_d = prenlp.preprocess(q.d)
print "Question: " + str(q.question)
print "A: " + str(q.a)
print "B: " + str(q.b)
print "C: " + str(q.c)
print "D: " + str(q.d)
# Generate Semantic Graph from Question
a = Association(filter="/c/en", limit=30)
semnet = a.get_similar_concepts_by_term_list(ques)
r = Result(semnet)
# Parse Similarity
similar = r.get_similar()
if len(similar) > 0:
# Splice Leading API Directory
for word in similar:
word[0] = word[0][6:]
print "\n"
print similar
print "\n"
# Compute Score Probabilities
prob = [
str(computeScore(similar, ans_a)),
str(computeScore(similar, ans_b)),
str(computeScore(similar, ans_c)),
