• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>Advances in data and web management >Short Text Clustering for Search Results
【24h】

Short Text Clustering for Search Results

机译：搜索结果的短文本聚类

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

An approach to clustering short text snippets is proposed, which can be used to cluster search results into a few relevant groups to help users quickly locate their interesting groups of results. Specifically, the collection of search result snippets is regarded as a similarity graph implicitly, in which each snippet is a vertex and each edge between the vertices is weighted by the similarity between the corresponding snippets. TermCut, the proposed clustering algorithm, is then applied to recursively bisect the similarity graph by selecting the current core term such that one cluster contains the term and the other does not. Experimental results show that the proposed algorithm improves the Kmeans algorithm by about 0.3 on Fscore criterion.
机译：提出了一种对短文本片段进行聚类的方法，该方法可用于将搜索结果聚类为几个相关的组，以帮助用户快速找到他们感兴趣的结果组。具体地，搜索结果片段的集合被隐式地视为相似性图，其中每个片段是一个顶点，并且顶点之间的每个边缘由相应片段之间的相似性加权。然后，通过选择当前的核心术语，将提议的聚类算法TermCut应用于递归平分相似度图，以便一个聚类包含该术语，而另一个不包含该术语。实验结果表明，该算法在Fscore准则上将Kmeans算法提高了约0.3。

著录项

  • 来源
  • 会议地点 Suzhou(CN)
  • 作者

    Xingliang Ni; Zhi Lu; Xiaojun Quan; Wenyin Liu; Bei Hua;

  • 作者单位

    Dept. of Computer Sci. and Tech., University of Sci. and Tech. of China, Hefei, China Department of Computer Science, City University of Hong Kong, HKSAR, China Joint Research Lab of Excellence, CityU-USTC Advanced Research Institute, Suzhou, China;

    Department of Computer Science, City University of Hong Kong, HKSAR, China;

    Department of Computer Science, City University of Hong Kong, HKSAR, China;

    Department of Computer Science, City University of Hong Kong, HKSAR, China Joint Research Lab of Excellence, CityU-USTC Advanced Research Institute, Suzhou, China;

    Dept. of Computer Sci. and Tech., University of Sci. and Tech. of China, Hefei, China Joint Research Lab of Excellence, CityU-USTC Advanced Research Institute, Suzhou, China;

  • 会议组织
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 计算机网络;
  • 关键词

    clustering; short text clustering; graph partitioning;

    机译：集群短文本聚类；图分区;

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