Towards grounding computational linguistic approaches to readability: Modeling reader-text interaction for easy and difficult texts
Towards grounding computational linguistic approaches to readability: Modeling reader-text interaction for easy and difficult texts
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奠定计算语言学方法的可读性：为简单和困难的文本建模读者与文本的交互
DOI：
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发表时间：
2016
期刊：
影响因子：
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通讯作者：
K. Scheiter
中科院分区：
文献类型：
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作者：
Sowmya Vajjala;Walt Detmar Meurers;Alexander Eitel;K. Scheiter
可读性评估的计算方法通常使用由出版商或教师标记的黄金标准语料库来构建和评估，而不是基于对人类表现的观察。考虑到阅读的过程和结果都是可以观察到的，有大量的经验可以用来为文本可读性的计算分析奠定基础。这也将支持明确的可读性模型，将文本复杂性和读者的语言熟练程度与阅读过程和结果联系起来。本文通过一项实验研究了文本复杂度和读者语言水平之间的关系如何影响读者的阅读过程和阅读后的表现结果。我们使用三个眼动跟踪变量：注视计数，平均注视计数和第二遍阅读持续时间来模拟阅读过程。我们的模型对这些变量的解释率分别为78.9%、74%和67.4%。通过回忆和理解问题对表现结果进行建模，这些模型分别解释了58.9%和27.6%的方差。虽然在线模型让我们更好地理解阅读与文本复杂性和语言熟练度的认知相关性，但离线测量的建模对于将用户方面纳入可读性模型特别相关。
Computational approaches to readability assessment are generally built and evaluated using gold standard corpora labeled by publishers or teachers rather than being grounded in observations about human performance. Considering that both the reading process and the outcome can be observed, there is an empirical wealth that could be used to ground computational analysis of text readability. This will also support explicit readability models connecting text complexity and the reader’s language proficiency to the reading process and outcomes. This paper takes a step in this direction by reporting on an experiment to study how the relation between text complexity and reader’s language proficiency affects the reading process and performance outcomes of readers after reading We modeled the reading process using three eye tracking variables: fixation count, average fixation count, and second pass reading duration. Our models for these variables explained 78.9%, 74% and 67.4% variance, respectively. Performance outcome was modeled through recall and comprehension questions, and these models explained 58.9% and 27.6% of the variance, respectively. While the online models give us a better understanding of the cognitive correlates of reading with text complexity and language proficiency, modeling of the offline measures can be particularly relevant for incorporating user aspects into readability models.
影响因子：
6.3
作者：
M. Just;P. Carpenter
通讯作者：
M. Just;P. Carpenter