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Mining Knowledge Bases for Question & Answers Websites.

机译:为问答网站挖掘知识库。

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摘要

We studied the problem of searching answers for questions on a Question-and-Answer Website from knowledge bases. A number of research efforts had been developed using Stack Overflow data, which is available for the public. Surprisingly, only a few papers tried to improve the search for better answers. Furthermore, current approaches for searching a Question-and-Answer Website are usually limited to the question database, which is usually the website own content. We showed it is feasible to use knowledge bases as sources for answers. We implemented both vector-space and topic-space representations for our datasets and compared these distinct techniques. Finally, we proposed a hybrid ranking approach that took advantage of a machine-learned classifier to incorporate the tag information into the ranking and showed that it was able to improve the retrieval performance.
机译:我们研究了从知识库在问答网站上搜索问题的答案的问题。利用Stack Overflow数据已经开展了许多研究工作,这些数据可供公众使用。令人惊讶的是,只有几篇论文试图改进对更好答案的搜索。此外,当前用于搜索问答网站的方法通常限于问题数据库,该问题数据库通常是网站本身的内容。我们证明了使用知识库作为答案的来源是可行的。我们为数据集实现了向量空间表示和主题空间表示,并比较了这些独特的技术。最后,我们提出了一种混合排序方法,该方法利用了机器学习的分类器将标签信息合并到排序中,并表明它能够提高检索性能。

著录项

  • 作者

    de Lima, Eduardo Coelho.;

  • 作者单位

    Rochester Institute of Technology.;

  • 授予单位 Rochester Institute of Technology.;
  • 学科 Computer science.;Information technology.;Information science.
  • 学位 M.S.
  • 年度 2016
  • 页码 57 p.
  • 总页数 57
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 公共建筑;
  • 关键词

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