Learning to Predict Readability using Diverse Linguistic Features
Learning to Predict Readability using Diverse Linguistic Features
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发表时间：
2010-08
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通讯作者：
Rohit J. Kate;Xiaoqiang Luo;Siddharth Patwardhan;M. Franz;Radu Florian;R. Mooney;S. Roukos;Chris Welty
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作者：
Rohit J. Kate;Xiaoqiang Luo;Siddharth Patwardhan;M. Franz;Radu Florian;R. Mooney;S. Roukos;Chris Welty
在本文中，我们考虑的问题，建立一个系统来预测自然语言文档的可读性。我们的系统使用基于语法和语言模型的各种功能进行训练，这些功能通常表示可读性。来自混合类型的文档数据集上的实验结果表明，当与经过语言训练的专家人类法官的预测相比时，学习系统的预测比天真的人类法官的预测更准确。实验还比较了不同的学习算法和不同类型的特征集用于预测可读性时的性能。
In this paper we consider the problem of building a system to predict readability of natural-language documents. Our system is trained using diverse features based on syntax and language models which are generally indicative of readability. The experimental results on a dataset of documents from a mix of genres show that the predictions of the learned system are more accurate than the predictions of naive human judges when compared against the predictions of linguistically-trained expert human judges. The experiments also compare the performances of different learning algorithms and different types of feature sets when used for predicting readability.