A Machine Learning Approach to Measurement of Text Readability for EFL Learners Using Various Linguistic Features.
A Machine Learning Approach to Measurement of Text Readability for EFL Learners Using Various Linguistic Features.
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使用各种语言特征测量 EFL 学习者文本可读性的机器学习方法。
DOI：
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发表时间：
2011
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通讯作者：
H. Isahara
中科院分区：
文献类型：
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作者：
Katsunori Kotani;T. Yoshimi;H. Isahara
本文介绍并评估了一种为 EFL（英语作为外语）学习者设计的可读性测量方法。所提出的可读性测量方法（回归模型）根据语言特征（例如词汇、句法和话语特征）估计文本可读性。文本可读性是指文本的理解率（0.0-1.0）。实验结果表明，所提出的可读性测量方法比基线方法具有更高的准确性，基线方法提供理解率数据分布的众数作为任何输入的估计值。
The present paper introduces and evaluates a readability measurement method designed for learners of EFL (English as a foreign language). The proposed readability measurement method (a regression model) estimates the text readability based on linguistic features, such as lexical, syntactic and discourse features. Text readability refers to the comprehension rate of a text (0.0-1.0). The experimental results showed that the proposed readability measurement method yielded higher accuracy than a baseline method, which provides the mode value of the distribution of the comprehension rate data as the estimated value for any input.