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Contextual retrieval of single Wikipedia articles to support the reading of academic abstracts.

机译：单个Wikipedia文章的上下文检索，以支持阅读学术摘要。

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摘要

Google style search engines are currently some of the most popular tools that people use when they are looking for information. There are a variety of reasons that people can have for conducting a search, although, these reasons can generally be distilled down to users being engaged in a task and developing an information need that impedes them from completing that task at a level which is satisfactory to them. The Google style search engine, however, is not always the most appropriate tool for every user task. In this thesis, our approach to search differs from the traditional search engine as we focus on providing support to users who are reading academic abstracts. When people do not understand a passage in the abstract they are reading, they often look for more detailed information or a definition. Presenting them with a list of possibly relevant search results, as a Google style search would, may not immediately meet this information need. In the case of reading, it is logical to hypothesize that users would prefer to receive a single document containing the information that they need. Developed in this thesis are retrieval algorithms that use the abstract being read along with the passage that the user is interested in to retrieve a single highly related article from Wikipedia. The top performing algorithm from the experiments conducted in this thesis is able to retrieve an appropriate article 77% of the time. This algorithm was deployed in a prototype reading support tool. LiteraryMark, in order to investigate the usefulness of such a tool. The results from the user experiment conducted in this thesis indicate that LiteraryMark is able to significantly improve the understanding and confidence levels of people reading abstracts.
机译：Google样式搜索引擎目前是人们寻找信息时使用的一些最受欢迎的工具。人们进行搜索可能有多种原因，尽管这些原因通常可以归结为从事某项任务的用户和发展出信息需求，从而阻碍他们以令人满意的水平完成该任务。他们。但是，谷歌风格的搜索引擎并非始终适合每个用户任务。在本文中，我们的搜索方法不同于传统的搜索引擎，因为我们专注于为阅读学术摘要的用户提供支持。当人们不理解正在阅读的摘要中的段落时，他们通常会寻找更详细的信息或定义。像Google样式的搜索一样，向他们显示可能相关的搜索结果列表可能无法立即满足此信息需求。在阅读的情况下，假设用户希望接收包含他们所需信息的单个文档是合乎逻辑的。本文开发了一种检索算法，该算法使用正在读取的摘要以及用户感兴趣的段落来从Wikipedia检索单个高度相关的文章。通过本文实验得出的最佳算法可以在77％的时间内检索到合适的文章。该算法已部署在原型阅读支持工具中。 LiteraryMark，以调查这种工具的有用性。本文进行的用户实验结果表明，LiteraryMark能够显着提高人们阅读摘要的理解度和置信度。

著录项

  • 作者

    Jordan, Chris.;

  • 作者单位

    Dalhousie University (Canada).;

  • 授予单位 Dalhousie University (Canada).;
  • 学科 Information Science.;Computer Science.
  • 学位 Ph.D.
  • 年度 2009
  • 页码 139 p.
  • 总页数 139
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 信息与知识传播;自动化技术、计算机技术;
  • 关键词

  • 入库时间 2022-08-17 11:38:30

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