【摘要】 针对传统问答系统答案抽取方式对答案片段的分词和上下文语义理解准确性的依赖严重,抽取过程耗费大量的人力和时间的问题,提出采用分步抽取答案的方法,先从答案片段中抽取包含答案的句子,再从提取的答案句中进行最终答案的抽取方式。在答案句抽取过程中使用Bi-LSTM(Bi-directional Long Short-Term Memory)和Max Pooling结合的方法构建答案句抽取模型。实验结果表明,在答案句的抽取中,该模型的MRR(Mean Average Precision)指数接近0. 75。更多还原

【Abstract】 In automatic question and answering system,traditional way of the answer extraction depends on participle of answer fragment and the accuracy of semantic comprehension from the context,which consumes manpower and time during the extraction process. To solve the above problems,the approach of step answer extraction is adopted where the final answer extraction is conducted through the extracted answers from the sentences. The model of answer sentence extraction is built in combination of Bi-LSTM(Bi-directional Long Short-Term Memory) and Max Pooling during the extraction process. The experimental results show that the MRR(Mean Average Precision) index of this model is close to 0. 75 in the extraction of the answer sentence.更多还原