def demonstrate_association():
print 'Demonstrating Association API'
a = Association(filter='/c/en/dog', limit=1)
data = a.get_similar_concepts('cat')
r = Result(data)
r.print_raw_result()
print
a = Association()
data = a.get_similar_concepts_by_term_list(['toast', 'cereal', 'juice', 'egg'])
r = Result(data)
r.print_raw_result()
print
r.parse_all_edges()
print
def demonstrate_association():
print 'Demonstrating Association API'
a = Association(filter='/c/en/dog', limit=1)
data = a.get_similar_concepts('cat')
r = Result(data)
r.print_raw_result()
print
a = Association()
data = a.get_similar_concepts_by_term_list(
['toast', 'cereal', 'juice', 'egg'])
r = Result(data)
r.print_raw_result()
print
r.parse_all_edges()
print
from conceptnet5_client.web.api import Search
from conceptnet5_client.web.api import Association
from conceptnet5_client.utils.result import Result
# get how similar cats and dogs
a = Association(filter='/c/en/dog', limit=1)
data = a.get_similar_concepts('cat')
r = Result(data)
# print results in key = value format
r.print_raw_result()
a = Association()
data = a.get_similar_concepts_by_term_list(['toast', 'cereal', 'juice', 'egg'])
r = Result(data)
# print results in key = value format
r.print_raw_result()
# Perform NLP Preprocessing
ques = prenlp.preprocess(q.question)
ans_a = prenlp.preprocess(q.a)
ans_b = prenlp.preprocess(q.b)
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"
# Perform NLP Preprocessing
ques = prenlp.preprocess(q.question)
ans_a = prenlp.preprocess(q.a)
ans_b = prenlp.preprocess(q.b)
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"
ques = prenlp.preprocess(q.question) ans_a = prenlp.preprocess(q.a) ans_b = prenlp.preprocess(q.b) ans_c = prenlp.preprocess(q.c) ans_d = prenlp.preprocess(q.d) print "[Question]: " + str(ques) + "\n" print "[Answer Choices]" print "A: " + str(ans_a) print "B: " + str(ans_b) print "C: " + str(ans_c) print "D: " + str(ans_d) print print "[Correct Answer]: " + str(q.ans) + "\n" # Perform Semantic Association Search on Question a = Association() data = a.get_similar_concepts_by_term_list(ques) r = Result(data) # Obtain Associtative Edges r.parse_all_edges() print # Perform an Exhaustive Entity Frequency Search # Check to see which one contains the most amount of relevant entities. #print "[Predicted Answer]: " #print "[Correct Answer]: " + str(q.ans) + "\n"
