首页> 外文学位 >Information foraging through clustering and summarization: A self-organizing approach.
【24h】

Information foraging through clustering and summarization: A self-organizing approach.

机译:通过聚类和汇总进行信息搜寻:一种自组织方法。

获取原文
获取原文并翻译 | 示例

摘要

Successful knowledge management requires efficient tools to manage information in the form of text. However, our productivity in generating information has exceeded our ability to process it, and the dream of creating an information-rich society has become a nightmare of information overload.; Although researchers and developers believe that interactive information access systems based on clustering and summarization offer a potential remedy to that problem, there is as yet no empirical evidence showing superiority of those tools over traditional keyword search.; This dissertation attempted to determine whether automated clustering can help to find relevant information by suggesting an innovative implementation and verifying its potential ability to be of help. Our implementation is based on Kohonen's self-organizing maps and acts as a visualization layer between the user and a keyword-based search engine. We used the clustering properties of self-organizing maps to create a summary of search results. The user relies on this summary when deciding whether and how to provide additional feedback to the system to obtain more relevant documents.; We have resolved multiple issues related to the speed and quality of output associated with self-organizing maps and created a version (Adaptive Search) that allows interactive Internet searching.; We have performed user studies and a controlled experiment in order to test the proposed approach. In a laboratory experiment, subjects spent less time finding correct answers using Adaptive Search than using the search engine directly. In addition, the documents containing answers were positioned consistently higher in the rank-ordered lists suggested by Adaptive Search as opposed to the lists suggested by the search engine. The search engine that we used was AltaVista, known to be one of the most popular, comprehensive and flexible engines on the Web.; Our main conclusion is that indeed information clustering helps information seekers if properly implemented.
机译:成功的知识管理需要高效的工具来管理文本形式的信息。但是,我们生成信息的生产力已经超出了我们处理信息的能力,创建信息丰富的社会的梦想已成为信息过载的噩梦。尽管研究人员和开发人员认为,基于聚类和汇总的交互式信息访问系统可以解决该问题,但尚无经验证据表明这些工具优于传统关键字搜索。本文试图通过提出创新的实施方案并验证其潜在的帮助能力,来确定自动聚类是否有助于查找相关信息。我们的实现基于Kohonen的自组织地图,并充当用户和基于关键字的搜索引擎之间的可视化层。我们使用自组织地图的聚类属性来创建搜索结果的摘要。用户在决定是否以及如何向系统提供其他反馈以获取更多相关文档时依赖此摘要。我们已经解决了与自组织地图相关的输出速度和质量相关的多个问题,并创建了一个允许交互式Internet搜索的版本(自适应搜索)。我们已经进行了用户研究和对照实验,以测试所提出的方法。在实验室实验中,与直接使用搜索引擎相比,使用“自适应搜索”花费的时间<斜体>更少的时间。另外,包含答案的文档在“适应性搜索”建议的排名列表中与搜索引擎建议的列表相反,其位置始终。我们使用的搜索引擎是AltaVista,它是网络上最受欢迎,最全面,最灵活的引擎之一。我们的主要结论是,信息聚类确实可以帮助信息检索者,如果正确实施

著录项

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