def answer_conversation(questions, tagmeToken, hyperparameters, number_of_frontier_nodes): answers = [] result = answer_complete_question(questions[0], tagmeToken) graph = gp.expand_context_with_statements(None, [result['context']], qa=True) answers.append(result['answers']) for counter, question in enumerate(questions[1:]): turn = counter + 2 answer, graph = answer_follow_up_question(question, turn, graph, hyperparameters, number_of_frontier_nodes) answers.append(answer) return answers
def determine_attributes(candidates, context, turn):
for candidate in candidates:
# create a temporal context and include the candidates' statement there
temp_context = context.copy()
temp_context = gp.expand_context_with_statements(temp_context, [candidate['statement']])
entity_nodes = gp.get_all_qa_nodes(temp_context)
if candidate['type'] == 'entity':
total_weighted_distance = 0
for entity_node in entity_nodes:
# increase distance by 1 to avoid zero division
distance = gp.get_distance(temp_context, candidate['entity'], entity_node[0])
total_weighted_distance += float(1/float(distance)) * turn_rating_higher_better(entity_node[1]['turn'], turn)
context_relevance = total_weighted_distance / float(len(entity_nodes))
priors = priors_of_entity(candidate['entity'])
elif candidate['type'] == 'qualifier_object':
total_weighted_distance = 0
for entity_node in entity_nodes:
# increase distance by 1 to avoid zero division
distance = gp.get_distance(temp_context, candidate['qualifier_object'], entity_node[0])
total_weighted_distance += float(1/float(distance)) * turn_rating_higher_better(entity_node[1]['turn'], turn)
context_relevance = total_weighted_distance / float(len(entity_nodes))
priors = priors_of_entity(candidate['qualifier_object'])
elif candidate['type'] == 'predicate':
# priors = priors_of_predicate(candidate['predicate'])
total_weighted_distance = 0
for entity_node in entity_nodes:
# every predicate label should be unique (to differ between them in the graph); predicate should already be in as in context
predicate_label = candidate['predicate'] + "-" + str(gp.predicate_nodes[candidate['predicate']]-1)
distance = gp.get_distance(temp_context, predicate_label, entity_node[0])
total_weighted_distance += float(1/float(distance)) * turn_rating_higher_better(entity_node[1]['turn'], turn)
context_relevance = total_weighted_distance / float(len(entity_nodes))
priors = priors_of_predicate(candidate['predicate'])
elif candidate['type'] == 'qualifier_predicate':
# priors = priors_of_predicate(candidate['qualifier_predicate'])
total_weighted_distance = 0
for entity_node in entity_nodes:
# every predicate label should be unique (to differ between them in the graph); predicate should already be in as in context
predicate_label = candidate['qualifier_predicate'] + "-" + str(gp.qualifier_predicate_nodes[candidate['qualifier_predicate']]-1)
distance = gp.get_distance(temp_context, predicate_label, entity_node[0])
total_weighted_distance += float(1/float(distance)) * turn_rating_higher_better(entity_node[1]['turn'], turn)
context_relevance = total_weighted_distance / float(len(entity_nodes))
priors = priors_of_predicate(candidate['qualifier_predicate'])
candidate['score'] = {'context_relevance': context_relevance , 'priors': priors}
return candidates