def answer_question(self, question_str):
question = Question()
question.set_question(question_str)
question = self.__question_classifier.classify(question)
logging.info('开始处理Question:' + question.get_question() + '问题类型:' +
str(question.get_question_type()))
if question.get_question_type() == QuestionType.Open:
return self.tl_chat(question_str)
if question.get_question_type() != QuestionType.Solution:
question = self.kb_based_answer_question(question)
else:
question = self.ir_based_answer_question(question)
logging.info('候选答案: ' + question.get_expect_answer())
return question.get_expect_answer()
def answer_question(self, question_str):
question = Question()
question.set_question(question_str)
question = self.__question_classifier.classify(question)
logging.info('开始处理Question:' + question.question_string +
question.question_type.value)
logging.info(question.body + question.medicine + question.disease)
if question.question_type == QuestionType.Medicine:
answer = self.kb_based_answer_question(question)
if not answer:
answer = self.ir_based_answer_question(question)
elif question.question_type == QuestionType.Definition \
and len(question.body+question.medicine+question.disease) == 1:
answer = self.baike_based_answer_question(
(question.body + question.medicine + question.disease)[0])
if not answer:
answer = self.ir_based_answer_question(question)
else:
answer = self.ir_based_answer_question(question)
del question
logging.info('候选答案: ' + answer)
return answer
:param question_a: 问题类
"""
logging.info('问句相似度开始')
question1 = question_a.word_list
question2 = question_b.word_list
class_score = class_sim(question_a.question_type, question_b.question_type)
key_score = key_sim(question_a, question_b)
wmd_score = wmd_sim(question1, question2)
# semantic_score = semantic_sim(question1, question2)
word_score = word_sim(question1, question2)
order_score = order_sim(question1, question2)
char_tfidf_score = char_tfidf_sim(question_a.question_string,
question_b.question_string)
score = weights['class_weight']*class_score + weights['key_weight']*key_score + weights['wmd_weight']*wmd_score + \
weights['word_weight']*word_score + weights['order_weight']*order_score + \
weights['char_tfidf_weight']*char_tfidf_score
logging.info('问句相似度结束,得分为{}\n\n'.format(score))
return score
if __name__ == '__main__':
q = Question()
q.set_question('感冒了怎么办')
que = input()
while que != 'end':
q2 = Question()
q2.set_question(que)
# q.set_question_type(QuestionType.Doctor)
combination_sim()
que = input()