Supercalifragilisticexpialidocious: Why Using the “Right” Readability Formula in Children’s Web Search Matters

Supercalifragilisticexpialidocious: Why Using the “Right” Readability Formula in Children’s Web Search Matters
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Supercalifragilisticexpialidocious:为什么在儿童网络搜索中使用“右”可读性公式很重要

DOI:
10.1007/978-3-030-99736-6_1
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发表时间:
2022
期刊:
44th European Conference on Information Retrieval (ECIR
影响因子:
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通讯作者:
Allen, Garrett and
Allen, Garrett and
中科院分区:
--
文献类型:
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作者:
Allen, Garrett and

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可读性是信息检索工具的核心组成部分,因为资源的复杂性直接影响其相关性:资源只有在用户能够理解的情况下才有用。作为一个进步知识的影响,可读性IR,我们专注于Web搜索forchildren。我们探讨如何传统的公式,这是简单的,高效的,便携式票价时,适用于估计的可读性,为儿童写的英文网页资源。然后,我们提出了一个公式非常适合儿童友好的Web资源的可读性估计。最后,我们实证表明,可读性可以动摇儿童的信息访问。这项工作的结果表明:(一)针对儿童的网络资源,一个简单的公式就足够了,只要它考虑到当代的术语和观众的要求,(二)而不是转向Flesch-Kincaid-一个流行的公式-使用的“正确”的公式可以塑造网络搜索工具,以最好地为儿童服务。我们在此提出的工作建立在三个支柱上:受众,应用和专业知识。它作为一个蓝图,放置可读性估计方法,最好地适用于和通知IR应用程序服务于不同的观众。
Readability is a core component of information retrieval (IR) tools as the complexity of a resource directly affects its relevance: a resource is only of use if the user can comprehend it. Even so, the link between readability and IR is often overlooked. As a step towards advancing knowledge on the influence of readability on IR, we focus onWeb searchforchildren. We explore how traditional formulas–which are simple, efficient, and portable–fare when applied to estimating the readability of Web resources for children written in English. We then present a formula well-suited for readability estimation of child-friendly Web resources. Lastly, we empirically show that readability can sway children’s information access. Outcomes from this work reveal that: (i) for Web resources targeting children, a simple formula suffices as long as it considers contemporary terminology and audience requirements, and (ii) instead of turning to Flesch-Kincaid–a popular formula–the use of the “right” formula can shape Web search tools to best serve children. The work we present herein builds on three pillars: Audience, Application, and Expertise. It serves as a blueprint to place readability estimation methods that best apply to and inform IR applications serving varied audiences.