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Assessing the relative reading level of sentence pairs for text simplification

机译:评估句子对的相对阅读水平以简化文本

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While the automatic analysis of the readability of texts has a long history, the use of readability assessment for text simplification has received only little attention so far. In this paper, we explore readability models for identifying differences in the reading levels of simplified and unsimpli-fied versions of sentences. Our experiments show that a relative ranking is preferable to an absolute binary one and that the accuracy of identifying relative simplification depends on the initial reading level of the unsimplified version. The approach is particularly successful in classifying the relative reading level of harder sentences. In terms of practical relevance, the approach promises to be useful for identifying particularly relevant targets for simplification and to evaluate simplifications given specific readability constraints.
机译:尽管自动分析文本的可读性已有很长的历史,但迄今为止,将可读性评估用于简化文本的方法很少受到关注。在本文中,我们探索了可读性模型,以识别句子的简化和未简化版本的阅读水平差异。我们的实验表明,相对排名优于绝对二进制排名,并且识别相对简化的准确性取决于未简化版本的初始阅读水平。该方法在分类较难句子的相对阅读水平方面特别成功。就实际相关性而言,该方法有望用于确定特别相关的简化目标,并在给定特定可读性的条件下评估简化情况。

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