Relevance Feedback Query Refinement for PDF Medical Journal Articles

A. Christiansen, D. J. Lee
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引用次数: 6

Abstract

This paper addresses relevance feedback as an alternative to keyword-based search engines for sifting through large PDF document collections and extracting the most relevant documents (especially for literature review purposes). Until now, relevance feedback has only been used in content-based image and video retrieval due to the inability to query those media types without keywords. Since PDF journal articles contain many valuable non-keyword features such as structure and formatting information as well as embedded figures, they would benefit from relevance feedback. Stripping a PDF into "full-text" for indexing purposes disregards these important features. We discuss how they can be used to our advantage and look to integrate the wealth of knowledge from relevance feedback text-based information retrieval. We argue for the benefits of placing the burden of relevance judgement on the user rather than the retrieval system and present alternative document views that quickly allow the user to deem relevance
PDF医学期刊文章的相关反馈查询改进
本文将相关性反馈作为基于关键字的搜索引擎的替代方案,用于筛选大型PDF文档集合并提取最相关的文档(特别是用于文献综述目的)。到目前为止,相关性反馈仅用于基于内容的图像和视频检索,因为无法查询那些没有关键字的媒体类型。由于PDF期刊文章包含许多有价值的非关键字特征,如结构和格式信息以及嵌入的图形,它们将受益于相关反馈。将PDF拆分成“全文”用于索引,忽略了这些重要的特性。我们将讨论如何利用它们来发挥我们的优势,并期望整合来自相关反馈的基于文本的信息检索的丰富知识。我们认为将相关性判断的负担放在用户而不是检索系统身上是有好处的,并提出了其他的文档视图,可以让用户快速判断相关性
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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