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A novel methodology for retrieving infographics utilizing structure and message content

机译:利用结构和消息内容检索图表的新颖方法

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Information graphics (infographics) in popular media are highly structured knowledge representations that are generally designed to convey an intended message. This paper presents a novel methodology for retrieving infographics from a digital library that takes into account a graphic's structural and message content. The retrieval methodology can be summarized thus: 1) hypothesize requisite structural and message content from a natural language query, 2) measure the relevance of each candidate infographic to the requisite structural and message content hypothesized from the user query, and 3) integrate these relevance measurements via a linear combination model in order to produce a ranked list of infographics in response to the user query. The methodology has been implemented and evaluated, and it significantly outperforms a baseline method that treats queries and graphics as bags of words. (C) 2015 Published by Elsevier B.V.
机译:流行媒体中的信息图形(infographics)是高度结构化的知识表示形式,通常用于传达预期的消息。本文提出了一种从数字图书馆检索信息图表的新颖方法,该方法考虑了图形的结构和消息内容。可以这样概括检索方法:1)从自然语言查询中假设必要的结构和消息内容,2)测量每个候选信息图与从用户查询中假设的必要结构和消息内容的相关性,以及3)整合这些相关性通过线性组合模型进行测量,以响应用户查询生成信息图表的排名列表。该方法已得到实施和评估,并且明显优于将查询和图形视为单词袋的基准方法。 (C)2015由Elsevier B.V.发布

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