• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Information Processing & Management >Optimal answerer ranking for new questions in community question answering
【24h】

Optimal answerer ranking for new questions in community question answering

机译：社区问答中新问题的最佳回答者排名

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Community question answering (CQA) services that enable users to ask and answer questions have become popular on the internet. However, lots of new questions usually cannot be resolved by appropriate answerers effectively. To address this question routing task, in this paper, we treat it as a ranking problem and rank the potential answerers by the probability that they are able to solve the given new question. We utilize tensor model and topic model simultaneously to extract latent semantic relations among asker, question and answerer. Then, we propose a learning procedure based on the above models to get optimal ranking of answerers for new questions by optimizing the multi-class AUC (Area Under the ROC Curve). Experimental results on two real-world CQA datasets show that the proposed method is able to predict appropriate answerers for new questions and outperforms other state-of-the-art approaches.
机译：使用户能够提出和回答问题的社区问题解答（CQA）服务已在Internet上流行。但是，许多新问题通常无法由适当的答复者有效解决。为了解决此问题路由任务，在本文中，我们将其视为排名问题，并根据潜在回答者能够解决给定新问题的可能性对他们进行排名。我们同时利用张量模型和主题模型来提取提问者，问题和回答者之间的潜在语义关系。然后，我们提出了一种基于上述模型的学习程序，通过优化多类AUC（ROC曲线下的面积）来获得针对新问题的答案的最佳排名。在两个真实世界的CQA数据集上的实验结果表明，所提出的方法能够预测新问题的合适答案，并且优于其他最新方法。

著录项

  • 来源
    《Information Processing & Management》 |2015年第1期|163-178|共16页
  • 作者

    Zhenlei Yan; Jie Zhou;

  • 作者单位

    Tsinghua National Laboratory for Information Science and Technology (TNList), State Key Laboratory on Intelligent Technology and Systems, Department of Automation, Tsinghua University, Beijing 100084, PR China;

    Tsinghua National Laboratory for Information Science and Technology (TNList), State Key Laboratory on Intelligent Technology and Systems, Department of Automation, Tsinghua University, Beijing 100084, PR China;

  • 收录信息
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Answerer ranking; Learn to rank; Tensor model; AUC; Community question answering;

    机译：回答者排名;学习排名;张量模型AUC;社区问答;

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号