Towards robust gaze-based objective quality measures for text
Towards robust gaze-based objective quality measures for text
复制标题
实现基于凝视的稳健文本客观质量测量
DOI：
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发表时间：
2012
期刊：
影响因子：
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通讯作者：
Georg Buscher
中科院分区：
文献类型：
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作者：
R. Biedert;A. Dengel;Mostafa Elshamy;Georg Buscher
越来越多的文本正在数字上读取。在本文中，我们探讨了如何使用眼睛跟踪设备来汇总许多读者的阅读数据，以便为作者和编辑提供客观且隐含地收集的质量反馈。我们提出了一种强大的方法，可以在各种与阅读相关的特征方面共同评估多个读者的目光数据。我们进行了一个实验，其中一群高中生随后由其他七名学生组成并评估并评估。分析记录的数据时，我们发现回归目标的量，阅读比率，阅读速度和阅读计数是最歧视性的特征，可以将非常可理解的文本段与几乎不可理解的文本段落区分开来。通过采用机器学习技术，我们能够以62％的总体准确性自动对文本的可理解性进行分类。
An increasing amount of text is being read digitally. In this paper we explore how eye tracking devices can be used to aggregate reading data of many readers in order to provide authors and editors with objective and implicitly gathered quality feedback. We present a robust way to jointly evaluate the gaze data of multiple readers, with respect to various reading-related features. We conducted an experiment in which a group of high school students composed essays subsequently read and rated by a group of seven other students. Analyzing the recorded data, we find that the amount of regression targets, the reading-to-skimming ratio, reading speed and reading count are the most discriminative features to distinguish very comprehensible from barely comprehensible text passages. By employing machine learning techniques, we are able to classify the comprehensibility of text automatically with an overall accuracy of 62%.