Estimating Contemporary Relevance of Past News

M. Sato, A. Jatowt, Yijun Duan, Ricardo Campos, Masatoshi Yoshikawa
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引用次数: 2

Abstract

Our society generates massive amounts of digital data, significant portion of which is being archived and made accessible to the public for the current and future use. In addition, historical born-analog documents are being increasingly digitized and included in document archives which are available online. Professionals who use document archives tend to know what they wish to search for. Yet, if the results are to be useful and attractive for ordinary users they need to contain content which is interesting and familiar. However, the state-of-the-art retrieval methods for document archives basically apply same techniques as search engines for synchronic document collections. In this paper, we introduce a novel concept of estimating the relation of archival documents to the present times, called contemporary relevance. Contemporary relevance can be used for improving access to archival document collections so that users have higher probability of finding interesting or useful content. We then propose an effective method for computing contemporary relevance degrees of news articles using Learning to Rank with a range of diverse features, and we successfully test it on the New York Times Annotated document collection. Our proposal offers a novel paradigm of information access to archival document collections by incorporating the context of contemporary time.
评估过去新闻的当代相关性
我们的社会产生了大量的数字数据,其中很大一部分被存档并向公众开放,以供当前和未来使用。此外,历史上的模拟文献正在越来越多地数字化,并被纳入在线提供的文献档案。使用文档档案的专业人员往往知道他们想要搜索什么。然而,如果要让搜索结果对普通用户有用且有吸引力,它们就需要包含有趣且熟悉的内容。然而,最先进的文档存档检索方法基本上与同步文档集合的搜索引擎应用相同的技术。在本文中,我们引入了一个新的概念来估计档案文件与当前时代的关系,称为当代相关性。当代相关性可用于改善对档案文件集合的访问,以便用户更有可能找到有趣或有用的内容。然后,我们提出了一种有效的方法来计算新闻文章的当代相关度,该方法使用一系列不同的特征来学习排名,我们成功地在《纽约时报》注释文档集合上进行了测试。我们的提案通过结合当代背景,为档案文件收藏提供了一种新的信息访问范式。
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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