%0 Journal Article
%T 融合pointwise及深度学习方法的篇章排序<br>Fusion of pointwise and deep learning methods for passage ranking
%A 庞博
%A 刘远超&lt
%A br&gt
%A PANG Bo
%A LIU Yuan-chao
%J 山东大学学报（理学版）
%D 2018
%R 10.6040/j.issn.1671-9352.1.2017.012
%X 摘要： 智能问答是让信息获取变得更加智能和便捷的重要途径,其中面向智能问答的篇章排序,对于准确把握用户查询意图,提升用户体验及答案反馈精度都有着十分重要意义。使用深度学习技术来捕获问题及篇章的语义信息,并以此构建到标签的映射模型,然后用训练好的模型来预测新的问题与篇章间的相关度,最后利用预测得到的篇章和问题的相关度指标来对同一问题对应的多个答案篇章进行排序。实验表明该方法在DCG@3指标上可以达到3.979,DCG@5达到5.396。<br>Abstract: Intelligent question answering is an important way to make information acquisition more intelligent and convenient. Intelligent Q&A oriented passage ranking is very important for accurately grasping the user's query intention, improving the user experience and the accuracy of feedback. We use deep learning techniques to capture semantic information about query and passages, and build the mapping model to the tag. Then the training model is used to predict the correlation between new query and the passage. Finally, we use the predicted correlation index of the passages and the query to sort the multiple answers of the same question. The experimental results show that our method can reach 3.979 on DCG@3 and 5.396 for DCG@5
%K 深度学习
%K 篇章排序
%K 相关度
%K &lt
%K br&gt
%K deep learning
%K passage ranking
%K relevance
%U http://lxbwk.njournal.sdu.edu.cn/CN/10.6040/j.issn.1671-9352.1.2017.012