Classifying Salient Textual Entities in the Headlines and Captions of Grouped Bar Charts
Classifying Salient Textual Entities in the Headlines and Captions of Grouped Bar Charts
复制标题
对分组条形图的标题和说明文字中的显着文本实体进行分类
DOI：
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发表时间：
2015
期刊：
影响因子：
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通讯作者：
S. Schwartz
中科院分区：
文献类型：
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作者：
Richard Burns;S. Carberry;S. Schwartz
信息图形（例如分组条形图）通常具有当它们出现在流行媒体中时旨在传达的交流信息。通信信号通常被设计到图形中，以帮助向图形查看者传达这些预期的消息。我们设计并实现了一个系统，该系统可以根据从图表中自动提取的通信信号来自动假设分组条形图的预期消息。对我们系统的分析表明，文本证据（例如图形标题或标题中提到的图形实体）是我们系统中最重要的证据。本文描述了一种支持向量机分类器，它采用图形及其标题和说明文字，并预测实体在语言上是否显着。
Information graphics, such as grouped bar charts, generally have a communicative message that they are intended to convey when they appear in popular media. Communicative signals are typically designed into the graphic to help convey to the graph viewer these intended messages. We have designed and implemented a system that automatically hypothesizes the intended message of a grouped bar chart from communicative signals that are automatically extracted from the graph. Analysis of our system revealed that textual evidence, such as graph entities mentioned in the headline or caption of the graphic, was the most important piece of evidence in our system. This paper describes a support vector machine classifier that takes a graph and its headlines and captions and predicts whether an entity is linguistically salient.